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![14 ! Understanding Social Networks
who are connected with one another tend to be physically proximate. People who are
near to one another also tend to share the same characteristics, values, and social
statuses. This relationship is one of the reasons, for example, that desegregating neigh-
borhoods or integrating schools can be particularly challenging. Regardless of how the
connection happens, once people are linked, there is a tendency for them to acquire
the same characteristics, values, or social statuses from one another" Chicken-and-egg
problems abound in social network analyses. •''
The discussion above concerns "people," but the concepts apply tro all levels of net-
works including groups, organizations, and even nations. Some illustrations and a few
elementary propositions will be offered for each concept. Further applications of the
concepts, as well as more complex concepts and propositions, will be developed in later
chapters devoted to specific topics. While all the concepts have formalized means of
measurement, and often several different ways to measure the same concept, the aim
of this book is to develop the concept itself and show how it is applied in theoretical
statements and in substantive findings. Measurement issues will be noted and
referenced, but the mechanics of the analysis is mainly reserved for other literature.1
What Is a Network?
We begin with a more precise definition of "network": a network is a set of relation-
ships. More formally, a network contains a set of objects (in mathematical terms,
nodes) and a mapping or description of relations between the objects or nodes. The
simplest network contains two objects, 1 and 2, and one relationship that links them.
Nodes 1 and 2, for example, might be people, and the relationship that' links them
might be as simple as standing in the same room. If 1 is in the same room as 2, then 2
is in the same room as 1.2
The relationship is in figure 2.1a is not directional.
FIGURE 2.IA Simple Relationship
There are also directional relationships (figure a.i.b) such as 1 likes 2.
(b)
FIGURE 2.IB Directed Relationship
In this simple network of "liking," the relationship could be
Bask Network Concepts, Part I j 15
Nodes 1 and 2 like one another, or their liking is mutual. The liking network below
(figure 2.1c) is similar to the first one of standing in the same room together, but has
a valence or a flow. Mutuality is a tricky matter, however, and not all that easy to
achieve, so mutual networks tend to be limited. A prevalent tie between dyads is anti-
symmetric. Father and son, boss and employee are examples. The relationship is by
definition different, depending on which way you look.
(c)
• • i
FIGURE 2.1C Symmetric Relationship
There need not be just one relationship mapped between nodes 1 and 2. For exam-
ple, 1 and 2 might be in the same room and might also like one another. When there is
more than one relationship, this is called a multiplex relationship.
Aside from their directionality, or lack of it, relationships might be more than the
sharing of an attribute or being in the same place at the same time,.There can be a flow
between the objects or the nodes. Liking, for example, might lead to an exchange of
gifts. Flows and exchanges are very important in network theory.
At one level, this list of concepts of relationships between pairs of nodes is now logi-
cally complete. But consider a network (figure 2.id) between pairs that operates via an
intermediary node. For example:
symmetrical.
FIGURE 2. ID Relationship Through an Intermediary
Node 1 is connected to 3 via 2. The relationships shown above are directional and
not reciprocal. They might be transitive or they might not* be. If the relationship is
transitive, it means that if 1 loves 2, then 2 also loves 3. Possible, but not likely. Tran-
sitive relationships are more common in an official hierarchy. Node 1 gives a message
to 2 who forwards it to 3.
One can describe the network distance between pairs of nodes in terms of the
number of steps or links between them. There are obviously two steps between 1 and
3- But if 1 also likes 3, as shown below (figure 2.ie), the network is said to be transitive
or balanced and mutual and, in this case, all three nodes are directly linked.
The network depicted in figure 2.ie is a "sociogram"—a term invented by Jacob
Moreno (Moreno 1953 [1934]), who is regarded by many as a key founder of modern
network studies. It is also what mathematicians call a graph. There is a branch of](https://image.slidesharecdn.com/2463817-250609150855-9dfbdffe/75/Understanding-Social-Networks-Theories-Concepts-And-Findings-Charles-Kadushin-17-2048.jpg)
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- 6. O X PO R D UNIVERSITY PRESS Oxford University Press, Inc., publishes works that further Oxford University's objective ofexcellence in research, scholarship, and education. Oxford N e wYork Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi N e w Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Copyright © 2012 by Oxford University Press Publishedby Oxford University Press, Inc. 198 Madison Avenue, NewYork, N e wYork 10016 www.oup.com Oxfordis a registered trademark ofOxford University Press Allrights reserved. Nopart ofthis publication maybe reproduced, storedin a retrieval system, or transmitted, in any form orbyanymeans, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission ofOxford University Press. ForGhislaineBoulanger The author and publishergratefullyacknowledgepermission to quotefromoruse previouslypublished materialunder copyright. Elsevier Limitedgrantedpermission to use materialfromthe author's published articles irfSocialNetworks and forpermission to quotefromBorgatti, Stephen P., and Josd-Luis Molina. 2005. Towardethical guidelines for networkresearch in organizations. SocialNetworks 27 (2):io7-ii7; Kalish, Yuval and GarryRobins. 2006. Psychological predispositions andnetwork structure: The relationship between individual predispositions, structuralholes and networkclosure. SocialNetworks 28:56-84.; andValente, Thomas W . 1996. Social networkthresholds in the diffusion of innovations. SocialNetworks 18:69-89. Taylorand Francis grantedpermission to quotefromGranovetter, Mark, and Roland Soong. 1983. Threshold models ofdiffusion andcollective behavior. JournalofMathematicalSociology 9:165-179. Wolters Kluwer Health grantedpermission to quotefromRothenberg, Richard B., Claire Sterk, Kathleen E. Toomey, John J. Potterat, David Johnson, MarkSchrader, and Stefani Hatch. 1998. Usingsocial networkand ethnographic tools to evaluate syphilis transmission. Sexually TransmittedDiseases 25 (March):i54-i6o. Library ofCongress Cataloging-in-Publication Data Kadushin, Charles. Understanding social networks: theories, concepts, and findings / Charles Kadushin. p. cm. Includes bibliographicalreferences and index. I S B N 978-0-19-537946-4 (cloth: alk. paper)—ISBN 978-0-19-537947-1 (pbk.: alk. paper) 1. Socialnetworks. I. Title. HM741.K33 2011 302.3—dc22 2011014668 1 3 5 7 9 8 6 4 2 Printed in the United States ofAmerica on acid-free paper
- 7. C o nt e n t s Preface xi 1. Introduction 3 Getting Connected 5 Networks as Information Maps 6 Leaders and Followers 8 Networks as Conduits 8 The Point of View 10 2. Basic Network Concepts, Part I: Individual Members of Networks 13 Introduction 13 What Is a Network? 14 Sociological Questions about Relationships 17 Connections 17 Propinquity 18 Homophily 18 Individual-Level Homophily 19 Homophily and Collectivities 20 Dyads and Mutuality 21 Balance and Triads 22 Where We Are Now 26 3. Basic Network Concepts, Part II: Whole Social Networks 27 Distributions 29 Dyads and Triads 29 Density 29 Structural Holes 29 Weak Ties 30
- 8. m m viii jContents "Popularity" or Centrality 31 Distance 32 Size of the Interpersonal Environment 34 The "Small World" 35 Multiplexity 35 Roles and Positions 38 Named Positions and Relationships 38 <, Informal Positions and Relationships 39 Informal Relations and Hierarchies 39 Embeddedness of the Informal within Instituted or Named Networks 40 Observed Roles 41 Summary 42 4. Basic Network Concepts, Part III: Network Segmentation 44 Introduction 44 Named and Unnamed Network Segments 45 Primary Groups, Cliques, and Clusters 46 Segmenting Networks from the Point of View of the Observer 46 Segmenting Groups on the Basis of Cohesion 47 Resistance to Disruption 48 Structural Similarity and Structural Equivalence 49 Core/Periphery Structures 50 Where We Are Now 55 5. The Psychological Foundations of Social Networks 56 Getting Things Done 57 Community and Support 58 Safety and Affiliation 59 Effectiveness and Structural Holes 59 Safety and Social Networks 60 Effectiveness and Social Networks 62 Both Safety and Effectiveness? 63 Driving for Status or Rank 65 Cultural Differences in Safety, Effectance, and Rank 66 Motivations and Practical Networks 66 Motivations of Corporate Actors 68 Cognitive Limits on Individual Networks 70 Where We Are Now 72 6. Small Groups, Leadership, and Social Networks: The Basic Building Blocks 74 Introduction 74 Primary Groups and Informal Systems: Propositions 75 Contents I ix Pure Informal Systems 77 How to Find Informal Systems 78 Asymmetric Ties and the Influence of the External System 82 Formalizing the System 85 Where We Are Now 88 7. Organizations and Networks 90 The Contradictions of Authority 91 Emergent Networks in Organizations 92 The Factory Floor 92 Information-Driven Organizations 94 Inside the Box, Outside the Box, or Both 100 Bridging the Gaps: Tradeoffs between Network Size, Diversity, and Social Cohesion 103 Where We Are Now 106 8. The Small World, Circles, and Communities 108 Introduction 108 How Many People Do You Know? 110 The Skewed Distribution of the Number of People One Knows 113 Formal Small World Models 119 Clustering in Social Networks 122 Social Circles 123 The Small World Search 128 Applications of Small World Theory to Smaller Worlds 130 Where We Are Now 132 9. Networks, Influence, and Diffusion 135 Networks and Diffusion—An Introduction 135 The Basic Model 137 Exogenous Factors in the Adoption of Innovations 139 Influence and Decision-Making 139 The Current State of Personal Influence 141 Self-Designated Opinion Leaders or Influentials 143 Characteristics of Opinion Leaders and Influentials 145 Group Influence 146 Epidemiology and Network Diffusion 148 Social Networks and Epidemiology 148 Social Networks and HIV-AIDS 150 Transporting Disease—Large-Scale Models 152 Tipping Points and Thresholds 153 Threshold 155 Where We Are Now 159
- 9. x I Contents 10.Networks as Social Capital 162 Introduction 162 The General Idea of Social Capital 164 Social Capital as an Investment 165 Individual-Level Social Capital 168 Social Support 168 Individual Networked Resources: Position and Resource Generators 170 Correlates of Individual Social Capital 172 i* Other Indicators of Networked Resources 173 Social Capital as an Attribute of Social Systems 175 Theorists of Social System Social Capital 175 BowlingAlone 177 Recent Findings on Social System Social Capital and Its Consequences 178 Where We Are Now 181 11. Ethical Dilemmas of Social Network Research 185 Networks as a Research Paradigm 185 Anonymity, Confidentiality, Privacy, and Consent 187 Who Benefits 189 Cases and Examples 191 Survey Research 191 Organization Research 194 Terrorists and Criminals 195 Networks and Terrorism: The CASOS Projects 196 Conclusion: More Complicated than the Belmont Report 198 12. Coda: Ten Master Ideas of Social Networks 201 Introduction 201 The Ten Master Ideas 202 NOTES 213 BIBLIOGRAPHY 223 INDEX 247 P r e f a c e WHEN I WAS a graduate student, before the term "social networks" came into general use, Paul F. Lazersfeld introduced his students to the idea of personal influence as a major factor in decision-making. Robert K. Merton had his seminar students read Georg Simmel line by line, among other matters explicating the ideas of triads and social circles. These teachings formed my introduction to social networks as problem- solving tools in understanding why people went to psychiatrists and how elites were organized. Hans Zetterberg insisted that socialtheory could and shouldbe systematic. I am grateful to these mentors for getting me started on what became the study of social networks as key methodological and theoretical insights that could help to unpack social phenomena. I remain as much interested in the impact of social net- works on social"structure and cultural content as I am in the study of networks them- selves. This book reflects that bias. The Cohen Center for Modern Jewish Studies at Brandeis University, my colleagues there, and especially the director, Leonard Saxe, were extremely supportive while I was writing this book making time and intellectual space for the effort. Deborah Grant, managing editor of the Center, turned the manuscript into readable prose. Katherine Ulrich was a meticulous copy editor. I am grateful to the Brandeis Libraries for their wide subscriptions to electronic journals and databases, making it easier to download an article than to go to my own library and find the hard copy version. Peter Prescott provided sage advice about publishing. The socialnetwork field is sobroadthat it is almost impossible for a single individual to encompass it and get everything right and make things understandable to the non- mathematically inclined. To the extent that I managed at all is because a number of people have helped and encouraged me. Foremost is James Moody who reviewed an XI
- 10. xii I Preface earlierversion of the manuscript and gently pointed out omissions and commissions, and gave me feedback from a class to which he assigned that earlier version. Thomas Valente introduced me to Oxford University Press, provided me with an early draft of his very useful book Social networks and health: Models, methods, and applications, and was helpful in many other ways. Claude Fischer reviewed the chapters on concepts and several others as well. Others reviewed one or more chapters and are acknowledged in a footnote at the beginning of each chapter. They include: Richard Alba, H. Russell Bernard, the late S. D. Berkowitz, Andrew Braun, Michael Brimm, Cynthia F. Epstein, Linton Freeman, Bethamie Horowitz, Dani Maman, Amalya Oliver, Stuart Pizer, Robert Putnam, Gary Robins, Theodore Sasson, Leonard Saxe, Tom Snijders, Barry Wellman, and Douglas R. White. Remaining errors and infelicities are of course my responsibility. My wife, Ghislaine Boulanger, who knows well the pangs of authorship, supported me throughout. Fishers Island, July, 2011. UNDERSTANDING SOCIAL NETWORKS
- 11. F it Introduction "NETWORKING" SEEMS TObe on everyone lips. No one simplygoes to aparty anymore. They go to network. For many people, the World Wide Web exists for the main purpose of making connections. Networking seems familiar yet mysterious, accessible yet arcane. Social networks, however, have been at the core of human society since we were hunters and gatherers. People were tied together through their relations with one an- other and their dependence on one another. Tribes, totems, and hierarchies may have come later. Kinship and family relations are social networks. Neighborhoods, villages, and cities are crisscrossed with networks of obligations and relationships. Beyond kin- ship relations, people in modern societies are dependent upon one another for such things as picking up the mail when one is away, help withfixingthe lawn mower, or rec- ommendations for good restaurants. Nonetheless, it is said that urban Americans are becoming more and more socially isolated. The metaphor of "bowling alone," rather than in clubs, leagues, or with friends; describes this picture of isolation and disengage- ment (Putnam 2000). But rather than disappearing*, neighborhood and village-based groups celebrated as the heart "ofnineteenth-centuryAmericahave become transformed from social relations and networks based on place or kinship into communities oriented around geographically dispersed social networks.1 The telephone and automobile started this revolution and were, not surprisingly, popular in rural areas where there were great*distances between households. We have been "networkers" for millennia. Networks <are not the same thing as "networking," or actively using a network to make connections to further one's personal goals. A network is simply a set of rela- tions between objects which could be people, organizations, nations, items found in a
- 12. 4 | UnderstandingSocial Networks Google search, brain cells, or electrical transformers. Transformers do not "network." In this book we are concerned with social networks, and what passes through these networks—friendship, love, money, power, ideas, and even disease. Has the internet, itself an example of a huge network, changed the rules of social networks? Less than has been claimed. Though people's networks contain substantial numbers of friends, neighbors, relatives, and workmates who are locally based, social networks are supplemented by new internet-based media. It is not a matter of one replacing the other; rather, the "internet fits seamlessly with in-person and phone en- counters .. .The more that people see each other in person and talKfpn the phone, the more they use the internet" (Boase et al. 2006). Social networks areresilient and con- stantly adapting. Large "mass societies" remain bound by personal ties. So while the mass media may have "discovered" social networks—a few years ago the New York Times celebrated social networks as one the "new ideas" of 2003 (Gertner 2003)—what is relatively new are systematic ways of talking about social networks, depicting them, analyzing them, and showing how they are related to more formal social arrangements such as organizations and governments. In 2008 alone, Science Citations Index found 1,269 articles on "social network" or "social networks." In the last 10 years, the total figure is 6,304. The growth is linear. Since 1984, there has also been rapid growth in the number of substantive areas to which social network analysis has been applied, from train schedules in China to the HIV epidemic. The popular press and blogs have been deluged with writing about social networks. Recently, Google listed over 52 million entries for "social networks." Nonetheless, there is something mysterious about social networks. We live sur- rounded by them, but usually cannot see more than one step beyond the people we are directly connected to, if that. It is like being stuck in a traffic jam surrounded by cars and trucks. The traffic helicopter can see beyond our immediate surroundings and sug- gest routes that might extricate us. Network analysis is like that helicopter. It allows us to see beyond our immediate circle. This book aims to take away some of the mystery about social networks by explaining the big ideas that underlie the social network phenomenon. I concentrate on the concepts, theories, and findings of the social network field. Intended for readers with no or a very limited background in mathematics or computers, this is not a "how to do it" book. There are manyuseful hooks that help the reader, often assuming the aid of an instructor, to ana- lyze, deconstruct, and display social networks with the aid of computers. Here I attempt rather to explain the concepts, theories, and findings developed by network experts. Be- cause I am a sociologist, the book has a structural social science bias, but it also takes account ofpeople and their motives. I hope itwillbe useful to social scientists whq encoun- ter social network research in their readingandwish to knowmore aboutit and to students new to the network field. I also hope it will be useful to managers, marketers, and ol^iers who constantly encounter social networks in their work life. Maybe avicLsodal "network- ers" will find it useful. Graphics-are important to the network field. So, when thereis a mathematical basis for network ideas and findings, I try to present them as a graph-. There is a lot to cover, and if the rapidgrowth of thefieldis anyguide, the comingyears will see even more work. There are two contrary trends in anyfield:investigators build on Introduction l 5 the basic work of others and stand on the shoulders of giants (Merton 1993), but at the same time they strive to make previous work obsolete. While recognizing that the social networkfieldis moving swiftly, I attempt to select material that serves as basic building blocks and examples of best practices that will allow the reader to understand and eval- uate new developments as they emerge. By the time the reader has finished the book, there maywell be important new discoveries in understanding social networks and their myriads of applications. Social network sites are burgeoning systems that in the hands of ordinary people as well as revolutionaries may—or may not—be changing the course of history. Yet sound research on these sites-is still in its infancy. My hope is that this book will give you the concepts and ideas to understand research and accounts of devel- opments in social networks that are now almost unimaginable. A few examples that dramatically capture the current state of the art of the field are the best way to begin to understand what social networks and social network research is all about: getting connected; networks as information maps; leaders and followers; and networks as conduits. Getting Connected Everyone could be connected, if only we knew how to reach out beyond our immediate horizons. One of the signs of the growth of the social network field is the very idea of "networking,"2 especially with the aid of the internet. Making connections through social network sites and the internet is ever increasing. It is not confined to adoles- cents looking for more friends. As of December 2008,75% of the U.S. adult population used the internet. Of these, 35% now have a profile on an online social network site, up more than fourfold from 8% in 2005.3 Seventy-five percent of adults 18 through 24 have a profile and almost 70% of students and teenagers, suggesting that as these co- horts age, the total proportion of the U.S. population using social network sites can only rise.4 Almost 90% of adults use their online profiles to keep up with friends, and half use it to make new friends. Internet-based social networking is more common among urban dwellers who perhaps might feel otherwise more isolated. Facebook, founded in 2004 as a social networking service for Harvard students, is currently (2010) valued at $50 billion dollars. According to the research firm ComScore's 2009 Digital Year in Review, it has surpassed MySpace and attracted 112 million visitors in December 2009, up over 100% during the year. As of July 2010, Facebook claimed 500 million active users worldwide. Twitter, publicly released in August 2006, is a social networking service that enables anyone to post messages of no more than 140 charac- ters, known as tweets, to people who sign up as self-designated followers. ComScore reported 20 million visitors for Twitter in December 2009, up tenfold from the previ- ous year. Google, the leading search engine that aids social networking services, is also based on network ideas first developed for citation analysis in the 1950s. All these sites and services are free to the user.5 Social network sites have profound implications. Suppose the number of "friends" you have on your profile is a modest 100. If none of them is a friend of the other, then
- 13. 6 | UnderstandingSocial Networks two steps removed, you have access to 10,000 people (100 times 100), who can also reach you. Three steps removed there are 1,000,000 (100 times 100 times 100). Soon, the entire world is a potential friend, for better or for worse. No wonder you hear from people who want to be your friend, many of whom you have never before heard from. There is some danger in this, for you may be exposed to more people than you may have desired. The world is indeed "small."'The implications for network theory will be expanded upon later in the book in a chapter on the "Small World." As "social networkers" hope, these connections can be useful. Connections have the potential to give a person access to valuable resources such as: referral to jobs by people out of one's immediate circle who might know of jobs one's close friends are unaware of (Granovetter 1973); ability to rise in the social ladder of occupations (Lin and Erickson 2008a); help with personal problems (Thoits 1995); referral to a good restaurant, book, or movie (Erickson 1996); or someone who can pick up your mail when you are away (Fis- cher 1982). These networked resources that you do not own, but to which you have access through your friends and acquaintances, are called "social capital" (Mouw 2006). Networks as Information Maps Social network analysis reveals what is hidden in plain sight. When you buy a book from Amazon, the site tells you what other books those who bought your selection also bought. I myself have succumbed to this marketing application of networks and bought books I might not have otherwise considered. Using network analysis principles, Valdis Krebs, an organization consultant who specializes in social network applications, ex- ploited the Amazon data to create on his blog6 a map of books related to the 2008 presi- dential campaign. Below is his map of books bought by the same people (figure 1.1). The M a r k e t sC o l l i d e F o o l i n gS o m eo ft h y eP e o p l eA l lo ftheT i m e P a r a d i g mf o rF i n a n c i a lM a r k e t s S h o c kD o c t r i n e C o n a d e n c eo fL i b e r a l F r e s hP i e c eo fH u m a n i t y B l a c kB e l tP a t r i o t i s m E c o n o m i cF a c t s a n dF a l l a c i e s Introduction I 7 arrows show, for example, that people who bought Dreams from My Father also bought Change We Can Believe In. There is an Obama cluster of books in the upper left corner; a Democratic campaign cluster in the middle, and a Republican group on the right. There is no overlap between the clusters. In 2008, the reading population of America was polarized, a significant augur for subsequent political polarization. Rules for Radicals, by radical community organizer Saul Alinsky, does not fit the partisan tone of the rest of the books in the Republican cluster and was bought by people who bought anti-Democrat and anti- Obama books. One supposes that they wanted to learn about some of the successful grassroots organizing principles of the Left. "Tea Party" organizers? Network ideas are useful for displaying data such as who bought what book but are especially helpful in making sense of news that involves connections, such as who was involved in what banking deal, who was tied to Madoff's Ponzi scheme, or who was in the network of 9/11 highjackers. Newspapers and Web news sites increasingly use them. Displays of networks on the Web are especially useful because they can be inter- active, allowing further information about the points in the network. We can not do this in a book, but figure 1.2, provides an example from Slate of the network implied in the Mitchell Report7 that connected baseball trainers and players involved in providing FIGURE 1.1 Books Bought by the Same People in the 2008 Presidential Campaign with the kind permission ofValdis Krebs PIGURE 1.2 Detail from a Network Depicting Connections in Performance-Enhancing Drug Usage in Baseball. With the kind permission ofWashingtonpost.newsweek Interactive (WPNI), publisher ofSlate Magazine Slate: The steroids social network. An interactive feature on the Mitchell report. By Adam Perer and Chris Wilson Updated Friday, December 21, 2007, at 11:12 A M ET. The figure is produced by Social Action software developed byThe Human-Computer Interaction Lab ofthe University of Maryland.
- 14. 8 ! UnderstandingSocial Networks or using performance-enhancing drugs. The full network diagram gives at a glance the information contained in the lengthy report. We are all familiar with another kind of network display: the organizational chart that shows who reports to whom and who is responsible for what. One of the earlier applications of social network studies was to discover how workplaces and organiza- ' tions really worked and what made for leadership. The formal chart cannot possibly account for workplace complexities, and strict adherence to them is usually a recipe for stasis. One of the latest buzzwords in management is "Network Organization," meaning an organization that is explicitly non-hierarchical. |/ What is a formal organization and what is an informal network has been the subject of a recent Supreme Court case.8 A man convicted, under federal racketeering laws, of breaking into safe deposit boxes claimed that his loosely organized group was not really an organization under the law. For the majority, upholding the conviction, Jus- tice Alito wrote, "The group need not have a name, regular meetings, dues, established rules and regulations, disciplinary procedures, or induction or initiation ceremonies." The core group was "loosely and informally organized, lacking a leader, hierarchy, or any long-term plan."9 Leaders and Followers Not long ago, Valdis Krebs started to utilize Twitter. Then there was a "denial of ser- vice" when Twitter broke down. He began to wonder about the failure of such services as Google, Facebook, and other sites that rely on a single site. They play with what he calls the betweenness paradox. Ultimate control when they work—total failure when they don't. Figure 1.3 from his blog10 shows a network with a number of obvious failure points. Take out one point, and many others become unconnected. This is also true of covert intelligence and terrorist networks. Take out a key point, and the network be- comes ineffective. Networks as Conduits Networks are conduits of both wanted and unwanted flows. Physicists who recently became interested in networks, social and other, were intrigued by the rapid failures of the North American electrical power grid leading to massive blackouts. Although the failures were eventually traced to one or two individual electrical transformers, it was determined that the design of the power network caused those failures to cascade into a system-wide breakdown (Watts 2003,19-24). Obesity can be an "epidemic." Network magic is no more evident than in the study that shows that over time, obese people are socially connected with other obese people (Christakis and Fowler 2007, 373). Network diagrams covering a period'starting from the year 1975 show that the tendency of obese people to be mutually connected Introduction j 9 c ^ - P k r ~< FIGURE 1.3 Kreb's Followers on Twitter. Copyright © Valdis Krebs, with his kind permission dramatically increases over time. This illustrates two of the major propositions of social networks: homophily—-people with like characteristics tend to be connected; and influence—connected people tend to have an effect on one another.11 The example of body mass is unexpected since that does not appear at first glance to be a social characteristic. But network analysis reveals that it is. These investigators showed, with the same Framinghain* study, originally intended as a prospective study of how heart and hypertension problems develop, that non-smoking, a "good" attribute, also tends to be contagious. Marketers are always trying to find ways to reach and persuade individuals in a mass society. Personal contact is most effective, if one can find a way to start a snow- ball rolling. Borrowing from epidemiology, marketers call this "viral marketing." In the network below (figure 1.4), researchers tracked recommendations for a Japanese graphic novel and illustrated the spread in a dense network. The full story is compli- cated, and viral marketing does not always work, but the investigators found that
- 15. io I UnderstandingSocial Networks mm p m & 5 0 ® 0 0 0*-®-*0 FIGURE 1.4 Personal Recommendation: Viral Marketing for a Japanese Graphic Novel Jure, Leskovec, A. Adamic Lada, and A. Huberman Bernardo. 2007. The dynamics of viral marketing. ACM Trans. Weh 1 (i):5 dobio.1145/1232722.1232727. © 2007 Association for Computing Machinery, Inc. Reprinted by permission. "personal recommendations are most effective in small, densely connected commu- nities enjoying expensive products" (Jure, Lada, and Bernardo 2007,36). You can see how the recommendations for the novel fanned out from a few key points in the network. The Point of View I have described just a few striking aspects of social networks. This book seeks more systematically to locate social networks within the general enterprise of social science. The structural point of view in social science sees the patterning of connections as both a cause and a consequence of human behavior: I hang out with people who share my ideas; but by virtue of hanging out with them, my ideas become more and more like their ideas. This view differs from a purely structural analysis of nonhuman networks. The mas- sive power failures that affected the North American power grid in August 2003 were not a failure of individual transformers but a failure of the verypatterning of the power network, that is, a design problem in the way the whole network was structured. Trans- formers, of course, have no "motivations," though when their functional designed Introduction 11 parameters are exceeded as a result of system overload, they fail and in part become responsible for a blackout. Human networks are also subject to structural analysis. But there is a difference. Human networks arise as a result of acts by individuals and organizations. The net- works created by these acts in turn produce networks that have consequences for indi- viduals and social organizations. Social networks evolve from individuals interacting with one another but produce extended structures that they had not imagined and in fact cannot see. Individual interaction takes place within the context of social statuses, positions, and social institutions, and so social networks are constrained by these factors. The social statuses, positions, and social institutions, however, can themselves be regarded as connected networks. These networks are constantly emerging and as a result affect and change the very institutions and organizations from which they emerged. Understanding a feedback system requires that one starts somewhere. The book is based on the assumption that social networks begin with people. One could of course argue, as indeed some do, that the network patterning itself produces individual moti- vation such as status seeking. But we prefer to start with people rather than with large social systems and build up from people and small groups to larger social systems.12 After explanations of key social network concepts in chapters 2, 3, and 4, chapter 5 deals with the psychological foundations of social networks: people, their motivations to form connections, and the cognitive limitations that affect the size of their social circles. We build on this with chapter 6 on small groups and leadership, which shows how these elementary building blocks draw on basic characteristics of individual moti- vation. Chapter 7 on organizations shows how patterning in small groups affects the function and structure of organizations. We then move to considering whole social sys- tems. Chapter 8 explains how the "small world" works and its implications. Chapter 9 covers diffusion through networks of artifacts, ideas, attitudes, and disease. Chapter 10 on social capital summarizes some of the utility of social networks as assets analogous to economic capital. In the end, most social network data comes from people. Since there are ethical issues in revealing personal connections, chapter 11 deals the compli- cations and ethics of gathering network data about people. The Coda, chapter 12, sums up the ten master ideas of social networks. As a rule, we generally eschew explaining a phenomenon by pure patterning and structure. There is a constant feedback between structure and behavior. Because of this feedback, network analysis gives us powerful tools and concepts to unravel mat- ters of concern to classical social theory, though to be sure, this is a work in progress and we have a long way yet to go. Some of the questions that are addressed, though hardly solved, are: What is the relationship between basic personality constructs and social relations? How do groups form? What is the nature and source of leadership? How can we best describe the way social positions relate to one another? What is the nature of authority in organizations and society? What are the ways of constructing efficient organizations that benefit their stakeholders? What is the nature of commu- nity, and how are people world-wide related to one another? How do new ideas spread and develop? What are the basic social resources of individuals and societies and how
- 16. 12 i UnderstandingSocialNetworks can they best be utilized? From time to time, we will reference classic social theorists to see to what extent social network ideas illuminate the problems that they posed. In this view, social networks are not only structural abstractions and the study of net- works is not an alternative to classic ways of understanding society, but is a way of gaining greater insight into social life. Though networks also characterize the inani- mate world such as electrical power grids, social networks have to*!qe understood, to quote E. F. Schumacher (1973), "as if people mattered." */ '' 1 Basic Network Concepts, Part I INDIVIDUAL MEMBERS OF NETWORKS Introduction Social network theory is one of the few theories in social science that can be applied to a variety of levels of analysis from small groups to entire global systems. The same powerful concepts work with small groups, with organizations, nations, and interna- tional systems. Chapters 2-4 introduce elementary network concepts, the "score-card" without which you cannot distinguish the players. In addition to defining the concepts, the (.chapters provide some flavor of how they are used and how they are linked to basic g.ideas about networks. This chapter introduces concepts concerning relations between 'the units that comprise a network. Chapter 3 discusses concepts that describe a net- work as a whole. Chapter 4 addresses where to draw the line—partitioning whole net- works. We begin with a definition of a simple network that connects pairs or dyads. We ^Conclude the chapter with a discussion of triads, the most elementary network in Which the structure of the network really matters. For networks, dyads and triads are f the analogue of molecules. Dyads and triads will give us a handle for understanding [larger networks. With dyads or pairs we are interested in why people come together—why they form |a dyad in thefirstplace."As with all network theory, we will see that a feedback loop is ttt the heart of network processes. There are forces such as propinquity—for example |«eing in the same place at the same time—that bring people together; but at the same jjtime, the dyad creates consequences for its members and for the whole network. People 1*3
- 17. 14 ! UnderstandingSocial Networks who are connected with one another tend to be physically proximate. People who are near to one another also tend to share the same characteristics, values, and social statuses. This relationship is one of the reasons, for example, that desegregating neigh- borhoods or integrating schools can be particularly challenging. Regardless of how the connection happens, once people are linked, there is a tendency for them to acquire the same characteristics, values, or social statuses from one another" Chicken-and-egg problems abound in social network analyses. •'' The discussion above concerns "people," but the concepts apply tro all levels of net- works including groups, organizations, and even nations. Some illustrations and a few elementary propositions will be offered for each concept. Further applications of the concepts, as well as more complex concepts and propositions, will be developed in later chapters devoted to specific topics. While all the concepts have formalized means of measurement, and often several different ways to measure the same concept, the aim of this book is to develop the concept itself and show how it is applied in theoretical statements and in substantive findings. Measurement issues will be noted and referenced, but the mechanics of the analysis is mainly reserved for other literature.1 What Is a Network? We begin with a more precise definition of "network": a network is a set of relation- ships. More formally, a network contains a set of objects (in mathematical terms, nodes) and a mapping or description of relations between the objects or nodes. The simplest network contains two objects, 1 and 2, and one relationship that links them. Nodes 1 and 2, for example, might be people, and the relationship that' links them might be as simple as standing in the same room. If 1 is in the same room as 2, then 2 is in the same room as 1.2 The relationship is in figure 2.1a is not directional. FIGURE 2.IA Simple Relationship There are also directional relationships (figure a.i.b) such as 1 likes 2. (b) FIGURE 2.IB Directed Relationship In this simple network of "liking," the relationship could be Bask Network Concepts, Part I j 15 Nodes 1 and 2 like one another, or their liking is mutual. The liking network below (figure 2.1c) is similar to the first one of standing in the same room together, but has a valence or a flow. Mutuality is a tricky matter, however, and not all that easy to achieve, so mutual networks tend to be limited. A prevalent tie between dyads is anti- symmetric. Father and son, boss and employee are examples. The relationship is by definition different, depending on which way you look. (c) • • i FIGURE 2.1C Symmetric Relationship There need not be just one relationship mapped between nodes 1 and 2. For exam- ple, 1 and 2 might be in the same room and might also like one another. When there is more than one relationship, this is called a multiplex relationship. Aside from their directionality, or lack of it, relationships might be more than the sharing of an attribute or being in the same place at the same time,.There can be a flow between the objects or the nodes. Liking, for example, might lead to an exchange of gifts. Flows and exchanges are very important in network theory. At one level, this list of concepts of relationships between pairs of nodes is now logi- cally complete. But consider a network (figure 2.id) between pairs that operates via an intermediary node. For example: symmetrical. FIGURE 2. ID Relationship Through an Intermediary Node 1 is connected to 3 via 2. The relationships shown above are directional and not reciprocal. They might be transitive or they might not* be. If the relationship is transitive, it means that if 1 loves 2, then 2 also loves 3. Possible, but not likely. Tran- sitive relationships are more common in an official hierarchy. Node 1 gives a message to 2 who forwards it to 3. One can describe the network distance between pairs of nodes in terms of the number of steps or links between them. There are obviously two steps between 1 and 3- But if 1 also likes 3, as shown below (figure 2.ie), the network is said to be transitive or balanced and mutual and, in this case, all three nodes are directly linked. The network depicted in figure 2.ie is a "sociogram"—a term invented by Jacob Moreno (Moreno 1953 [1934]), who is regarded by many as a key founder of modern network studies. It is also what mathematicians call a graph. There is a branch of
- 18. 16 j UnderstandingSocial Networks FIGURE 2.IE Sociogram ofThree Nodes, All Mutually Related mathematics, graph theory, which allows sociograms to be manipulated mathemati- cally (Harary, Norman, and Cartwright 1965). The depiction of relationships as so- ciograms allowed observers almost instant insight as to what was going on in small, not overly complicated networks. The addition of graph theory to the tools for un- derstanding networks further allowed for understanding and manipulating much larger and more complex networks. In this introduction to network theory, we will dispense with the mathematics of graph theory but will rely on its insights and find- ings. The simple network of three units is called a triad. This simple network turns out to be the building block of more complex relations and will be discussed at the end of this chapter. Many network analysts, and much of the software for manipulating networks, prefer to work algebraically with networks when they are depicted and expressed as matrices. Below (table 2.1) is the same sociogram but in matrix form. In network terms, we call this an adjacency matrix because it shows who is next to whom. The numbers, i, 2, and 3 on the top line and the first column identify the same nodes as in figure 2.2. The number 1 on the second line indicates a connection between the nodes. Node 1 "chooses" nodes 2 and 3. Node 2 "chooses" nodes 1 and 3. Node 3 "chooses" nodes 1 and 2. The dashes indicate that in this graph or matrix, self-choice is not at play, though in some networks self-choice can be an option. For example, <andi- dates in an election can vote for themselves. TABLE 2.1 The Adjacency Matrix that Represents Figure 1 i _ 1 _ _ _ a _ 3 _ j 1 1 1 12 1 - 1 l3 1 1 J ' ' Basic Network Concepts, Part I jp Sociological Questions about Relationships Though all of our examples thus far have been social, in principle, we might as well have been talking about electrical currents. There is a branch of network theory that deals with such matters, though electrical circuits tend to be considerably simpler than social networks. But consider. At each level of analysis—individual, organization, or nation-state, for example—what are the conditions that make it more or less likely that a path will exist between two nodes, that the nodes will have the same attributes, that they will be reciprocally or mutually related to one another, and that triads will be balanced? The answers lie in social theory. We now introduce some elementary hypotheses about these conditions. Social scientists have investigated three kinds of networks: ego-centric, socio- centric, and open-system networks. Ego-centric networks are those networks that are connected with a single node or individual, for example, my good friends or all the companies that do business with Widgets, Inc. (the favorite name of organiza- tions studied in business schools). However, a list is not necessarily a network. In popular discourse, especially when social support is discussed, any list is called a "network." It is a network in a basic sense because even if no one on the list is connected with one another, each individual is at least all connected with the person being supported. The support may include help with a job search, comfort during an illness, or a loan of money or a lawn mower. A person with a large number of good friends whom he or she can count on is commonly said to have a large "network." This network cannot be discussed in social network terms, however, unless we know whether and how these people are connected with one another.3 It is obviously one thing to have a supportive network in which most people know one another and a very different matter if the people are unknown to one another. Ego networks and how they extend further to links beyond the starting point are discussed in the next chapter on whole networks. Socio-centric networks are networks in a "box." Connections between children in a classroom or between executives or workers in an organization are closed system net- works and the ones most often studied in terms of the fine points of network struc- ture. These were' the ones with which Moreno began his studies. Open system networks are networks in which the boundaries are not necessarily clear, for they are not in a box—for example, the elite of the United States, connections between corpo- rations, the chain of infiuencers of a particular decision, or the adopters of new prac- tices. In some ways, these are the most interesting networks. Later chapters in this book, one about the "Small World" and one about diffusion, explore these open sys- tems in some detail. o CONNECTIOlJ-S We now examine some of the social situations and forces that make for connections between one node (e.g., a person, an organization, a country) to another.
- 19. Another Random Documenton Scribd Without Any Related Topics
- 23. The Project GutenbergeBook of Poisons, Their Effects and Detection
- 24. This ebook isfor the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this ebook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook. Title: Poisons, Their Effects and Detection Author: Alexander Wynter Blyth Release date: May 13, 2013 [eBook #42709] Most recently updated: October 23, 2024 Language: English Credits: Produced by Chris Curnow, Harry Lamé and the Online Distributed Proofreading Team at http://www.pgdp.net (This file was produced from images generously made available by The Internet Archive) *** START OF THE PROJECT GUTENBERG EBOOK POISONS, THEIR EFFECTS AND DETECTION ***
- 25. Please see Transcriber’sNotes at the end of this document.
- 27. POISONS: THEIR EFFECTS ANDDETECTION. BY THE SAME AUTHOR. Fourth Edition. At Press. FOODS: THEIR COMPOSITION AND ANALYSIS. With numerous Tables and Illustrations. General Contents. History of Adulteration—Legislation, Past and Present—Apparatus useful to the Food Analyst—“Ash”—Sugar—Confectionery—Honey— Treacle—Jams and Preserved Fruits—Starches—Wheaten-Flour— Bread—Oats—Barley—Rye—Rice—Maize—Millet—Potato—Peas— Chinese Peas—Lentils—Beans—Milk—Cream—Butter—Cheese—Tea— Coffee—Cocoa and Chocolate—Alcohol—Brandy—Rum—Whisky—Gin —Arrack—Liqueurs—Beer—Wine—Vinegar—Lemon and Lime Juice— Mustard—Pepper—Sweet and Bitter Almond—Annatto—Olive Oil— Water. Appendix: Text of English and American Adulteration Acts. “Will be used by every Analyst.”—Lancet. “Stands Unrivalled for completeness of information. . . . A really ‘practical’ work for the guidance of practical men.”—Sanitary Record. “An ADMIRABLE DIGEST of the most recent state of knowledge. . . . Interesting even to lay-readers.”—Chemical News. In Large 8vo, Handsome Cloth. 21s.
- 28. FORENSIC MEDICINE AND TOXICOLOGY. By J.DIXON MANN, M.D., F.R.C.P., Professor of Medical Jurisprudence and Toxicology in Owens College, Manchester; Examiner in Forensic Medicine in the University of London, and in the Victoria University; Physician to the Salford Royal Hospital. Part I.—Forensic Medicine. Part II.—Insanity in its Medico-legal Bearings. Part III.—Toxicology. “By far the MOST RELIABLE, MOST SCIENTIFIC, and MOST MODERN book on Medical Jurisprudence with which we are acquainted.”—Dublin Medical Journal. “A most useful work of reference. . . . Of value to all those who, as medical men or lawyers, are engaged in cases where the testimony of medical experts forms a part of the evidence.”—The Law Journal. London: Charles Griffin & Co., Ltd., Exeter St., Strand. POISONS: THEIR EFFECTS AND DETECTION. A MANUAL FOR THE USE OF
- 29. ANALYTICAL CHEMISTS AND EXPERTS. WITHAN INTRODUCTORY ESSAY ON THE GROWTH OF MODERN TOXICOLOGY. BY ALEXANDER WYNTER BLYTH, M.R.C.S., F.I.C., F.C.S., &c., BARRISTER-AT-LAW; PUBLIC ANALYST FOR THE COUNTY OF DEVON; AND MEDICAL OFFICER OF HEALTH AND PUBLIC ANALYST FOR ST. MARYLEBONE. THIRD EDITION, REVISED AND ENLARGED. With Tables and Illustrations. LONDON: CHARLES GRIFFIN AND COMPANY, LIMITED, EXETER STREET, STRAND. 1895. (All Rights Reserved.) D. VAN NOSTRAND COMPANY, NEW YORK.
- 31. PREFACE TO THETHIRD EDITION. The present edition, which appears on the same general plan as before, will yet be found to have been in great part re-written, enlarged, and corrected. Analytical methods which experience has shown to be faulty have been omitted, and replaced by newer and more accurate processes. The intimate connection which recent research has shown to exist between the arrangement of the constituent parts of an organic molecule and physiological action, has been considered at some length in a separate chapter. The cadaveric alkaloids or ptomaines, bodies playing so great a part in food-poisoning and in the manifestations of disease, are in this edition treated of as fully as the limits of the book will allow. The author, therefore, trusts that these various improvements, modifications, and corrections will enable “Poisons” to maintain the position which it has for so many years held in the esteem of toxicologists and of the medical profession generally. The Court House, St. Marylebone, W. June, 1895.
- 33. CONTENTS. PART I.—INTRODUCTORY. I. THEOLD POISON-LORE. Section Page 1. The History of the Poison-lehre—The Origin of Arrow-Poison—Greek Myths, 1 2. Knowledge of the Egyptians relative to Poisons —Distillation of Peach-Water, 2 3. Roman and Greek Knowledge of Poison— Sanction of Suicide among the Ancients—The Classification of Poisons adopted by Dioscorides, 2-4 4. Poisoning among Eastern Nations—Slow Poisons, 4, 5 5. Hebrew Knowledge of Poisons, 5 6. The part which Poison has played in History— Statira—Locusta—Britannicus—The Rise of Anatomy—The Death of Alexander the Great— of Pope Alexander VI.—The Commission of Murder given by Charles le Mauvais—Royal Poisoners—Charles IX.—King John—A Female Poisoner boiled alive, 5-9 7. The Seventeenth Century Italian Schools of Criminal Poisoning—The Council of Ten—John 9-13
- 34. of Ragubo—The ProfessionalPoisoner—J. B. Porta’s Treatise on Natural Magic—Toffana and the “Acquetta di Napoli”—Organic Arsenical Compounds—St. Croix and Madame de Brinvilliers—Extraordinary Precautions for the Preservation from Poison of the Infant Son of Henry VIII., II. GROWTH AND DEVELOPMENT OF THE MODERN METHODS OF CHEMICALLY DETECTING POISONS. 8. Phases through which the Art of Detecting Poisons has passed, 13 9. Treatise of Barthélémy d’Anglais—Hon. Robert Boyle—Nicolas l’Emery’s Cours de Chimie— Mead’s Mechanical Theory of Poisons—Rise of Modern Chemistry—Scheele’s Discoveries, 13, 14 10. History of Marsh’s Test, 14, 15 11. Orfila and his Traité de Toxicologie—Orfila’s Method of Experiment, 15 12. The Discovery of the Alkaloids—Separation of Narcotine, Morphine, Strychnine, Delphinine, Coniine, Codeine, Atropine, Aconitine, and Hyoscyamine, 15, 16 13. Bibliography of the Chief Works on Toxicology of the Nineteenth Century, 16-19 PART II. I. DEFINITION OF POISON.
- 35. 14. The LegalDefinition of Poison—English Law as to Poison, 20, 21 15. German Law as to Poisoning—French Law as to Poisoning, 21, 22 16. Scientific Definition of a Poison—The Author’s Definition, 22, 23 II. CLASSIFICATION OF POISONS. 17. Foderé’s, Orfila’s, Casper’s, Taylor’s, and Guy’s Definition of Poisons—Poisons arranged according to their Prominent Effects, 23, 24 18. Kobert’s Classification, 24, 25 19. The Author’s Arrangement, 25-28 III. STATISTICS. 20. Statistics of Poisoning in England and Wales during the Ten Years 1883-92—Various Tables, 28-31 21. German Statistics of Poisoning, 31-33 22. Criminal Poisoning in France, 33, 34 IV. THE CONNECTION BETWEEN TOXIC ACTION AND CHEMICAL COMPOSITION. 23. The Influence of Hydroxyl—The Replacement of Hydrogen by a Halogen—Bamberger’s Acylic and Aromatic Bases, 35, 36 24. The Replacement of Hydrogen by Alkyls in Aromatic Bodies, 36-38 25. The Influence of Carbonyl Groups, 39 26. Oscar Loew’s Theory as to the Action of Poisons, 39-41 27. Michet’s Experiments on the relative Toxicity of Metals, 41, 42
- 36. V. LIFE TESTS:OR THE IDENTIFICATION OF POISON BY EXPERIMENTS ON ANIMALS. 28. The Action of Poisons on Infusoria, Cephalopoda, Insects, 42-44 29. Effect of Poisons on the Heart of Cold-blooded Animals, 44, 45 30. The Effect of Poisons on the Iris, 45, 46 VI. GENERAL METHOD OF PROCEDURE IN SEARCHING FOR POISON. 31. Concentration in a Vacuum—Drying the Substance—Solvents—Destruction of Organic Matter, 46-50 32. Autenrieth’s General Process—Distillation— Shaking up with Solvents—Isolation of Metals —Investigation of Sulphides Soluble in Ammonium Sulphide—of Sulphides Insoluble in Ammonium Sulphide—Search for Zinc and Chromium—Search for Lead, Silver, and Barium, 50-53 VII. THE SPECTROSCOPE AS AN AID TO THE IDENTIFICATION OF CERTAIN POISONS. 33. The Micro-Spectroscope—Oscar Brasch’s Researches of the Spectra of Colour Reactions —Wave Lengths, 54-56 Examination of Blood or of Blood-Stains. 34. Naked-eye Appearance of Blood-Stains— Dragendorff’s Process for Dissolving Blood, 56, 57 35. Spectroscopic Appearances of Blood— Spectrum of Hydric Sulphide Blood—of Carbon Oxide Hæmoglobin—Methæmoglobin—of Acid 57-62
- 37. Hæmatin—Tests for COBlood—Piotrowski’s Experiments on CO Blood—Preparation of Hæmatin Crystals—The Guaiacum Test for Blood, 36. Distinction between the Blood of Animals and Men—The Alkalies in various Species of Blood, 62, 63 PART III.—POISONOUS GASES: CARBON MONOXIDE—CHLORINE— HYDRIC SULPHIDE. I. CARBON MONOXIDE. 37. Properties of Carbon Monoxide, 64 38. Symptoms—Acute Form—Chronic Form, 64-66 39. Poisonous Action on the Blood—Action on the Nervous System, 66, 67 40. Post-mortem Appearances, 67 41. Mass Poisonings by Carbon Monoxide—The Leeds Case—The Darlaston Cases, 67-70 42. Detection of Carbon Monoxide—The Cuprous Chloride Method—Wanklyn’s Method— Hempel’s Method, 70, 71 II. CHLORINE. 43. Chlorine; its Properties—The Weldon Process of manufacturing “Bleaching Powder,” 71, 72 44. Effects of Chlorine, 72 45. Post-mortem Appearances, 72 46. Detection of Free Chlorine, 72
- 38. III. HYDRIC SULPHIDE(SULPHURETTED HYDROGEN). 47. Properties of Hydric Sulphide, 72, 73 48. Effects of breathing Hydric Sulphide—Action on the Blood—The Cleator Moor Case, 73, 74 49. Post-mortem Appearances, 74 50. Detection, 74 PART IV.—ACIDS AND ALKALIES. Sulphuric Acid—Hydrochloric Acid—Nitric Acid— Acetic Acid—Ammonia—Potash—Soda—Neutral Sodium, Potassium, and Ammonium Salts. I. SULPHURIC ACID. 51. Varieties and Strength of the Sulphuric Acids of Commerce—Properties of the Acid— Nordhausen Sulphuric Acid, 75, 76 52. Properties of Sulphuric Anhydride, 76 53. Occurrence of Free Sulphuric Acid in Nature, 76 54. Statistics—Comparative Statistics of different Countries, 76, 77 55. Accidental, Suicidal, and Criminal Poisoning— Sulphuric Acid in Clysters and Injections, 77, 78 56. Fatal Dose, 78, 79 57. Local Action of Sulphuric Acid—Effects on Mucous Membrane, on the Skin, on Blood, 79, 80 58. Action of Sulphuric Acid on Earth, Grass, Wood, Paper, Carpet, Clothing, Iron—Caution 80, 81
- 39. necessary in judgingof Spots—Illustrative Case, 59. Symptoms—(1) External Effects—(2) Internal Effects in the Gullet and Stomach—Intercostal Neuralgia, 81-83 60. Treatment of Acute Poisoning by the Mineral Acids, 83 61. Post-mortem Appearances—Rapid and Slow Poisoning—Illustrative Cases, 83-85 62. Pathological Preparations in the different London Hospital Museums, 85, 86 63. Chronic Poisoning, 86 Detection and Estimation of Free Sulphuric Acid. 64. General Method of Separating the Free Mineral Acids—The Quinine Process—The Old Process of Extraction by Alcohol—Hilger’s Test for Mineral Acid, 87, 88 65. The Urine—Excretion of Sulphates in Health and Disease—The Characters of the Urine after taking Sulphuric Acid, 88-90 66. The Blood in Sulphuric Acid Poisoning, 90 67. The Question of the Introduction of Sulphates by the Food—Largest possible Amount of Sulphates introduced by this Means—Sulphur of the Bile—Medicinal Sulphates, 90, 91 II. HYDROCHLORIC ACID. 68. General Properties of Hydrochloric Acid— Discovery—Uses—Tests, 91, 92 69. Statistics, 92, 93 70. Fatal Dose, 93 71. Amount of Free Acid in the Gastric Juice, 93, 94
- 40. 72. Influence ofHydrochloric Acid on Vegetation— Present Law on the Subject of Acid Emanations from Works—The Resistant Powers of various Plants, 94 73. Action on Cloth and Manufactured Articles, 95 74. Poisonous Effects of Hydrochloric Acid Gas— Eulenberg’s Experiments on Rabbits and Pigeons, 95, 96 75. Effects of the Liquid Acid—Absence of Corrosion of the Skin—Pathological Appearances—Illustrative Cases, 96, 97 76. Post-mortem Appearances—Preparations in the different London Museums, 97, 98 77. (1) Detection of Free Hydrochloric Acid— Günzburg’s Test—A. Villiers’s and M. Favolle’s Test—(2) Quantitative Estimation, Sjokvist’s Method—Braun’s Method, 98-101 78. Method of Investigating Hydrochloric Acid Stains on Cloth, &c., 101, 102 III. NITRIC ACID. 79. Properties of Nitric Acid, 102, 103 80. Use in the Arts, 103 81. Statistics, 103 82. Fatal Dose, 104 83. Action on Vegetation, 104 84. Effects of Nitric Acid Vapour—Experiments of Eulenberg and O. Lassar—Fatal Effect on Man, 104, 105 85. Effects of Liquid Nitric Acid—Suicidal, Homicidal, and Accidental Deaths from the Acid, 105, 106 86. Local Action, 106 87. Symptoms—The Constant Development of Gas —Illustrative Cases, 106, 107
- 41. 88. Post-mortem Appearances—Preparationsin various Anatomical Museums, 107-109 89. Detection and Estimation of Nitric Acid, 109, 110 IV. ACETIC ACID. 90. Symptoms and Detection, 110 V. AMMONIA. 91. Properties of Ammonia, 111 92. Uses—Officinal and other Preparations, 111, 112 93. Statistics of Poisoning by Ammonia, 112 94. Poisoning by Ammonia Vapour, 112 95. Symptoms—Illustrative Case, 112, 113 96. Chronic Effects of the Gas, 113 97. Ammonia in Solution—Action on Plants, 113 98. Action on Human Beings and Animal Life— Local Action on Skin—Action on the Blood— Time of Death, 113-115 99. Post-mortem Appearances, 115 100. Separation of Ammonia—Tests, 115, 116 101. Estimation of Ammonia, 116 VI. CAUSTIC POTASH AND SODA. 102. Properties of Potassium Hydrate, 116, 117 103. Pharmaceutical Preparations, 117 104. Carbonate of Potash, 117 105. Bicarbonate of Potash, 117 106. Caustic Soda—Sodium Hydrate, 117, 118 107. Carbonate of Soda, 118 108. Bicarbonate of Soda, 118 109. Statistics, 118 110. Effects on Animal and Vegetable Life, 118, 119 111. Local Effects, 119
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