Notice bibliographique
Résumé
Citation (2019), "Index", Deflem, M. and Silva, D.M.D. (Ed.) Methods of Criminology and Criminal Justice Research (Sociology of Crime, Law and Deviance, Vol. 24), Emerald Publishing Limited, Bingley, pp. 195-200. https://doi.org/10.1108/S1521-613620190000024018 Publisher: Emerald Publishing Limited Copyright © 2019 Emerald Publishing Limited INDEX Index Access to information (ATI), 34 Adaptation, 190 Adolescents, 119 Advanced information technologies, 181 Advocacy research, 143–144 Age-graded life events on risk of elder financial exploitation, 109–110 Agency records, 166 challenges in human trafficking research, 171–172 to estimating prevalence, 172–175 research on human trafficking using, 169–172 types, 167–169 Agency-based male sex workers, 88 Alexa Traffic Ranks, 93–94 Alliances, 59 networks, 48 “American Dream”, 74–75 Arrest records, 50–51 Assaults, 72 Assets, 104 ATI /FOI laws, 34–35, 37, 41–42 Audio-based online material, 181 BeautifulSoup, 182 “Big data” phenomenon, 187 Black Widow, 182 Boston Special Youth Project, 50 Campus Quality of Life Survey (CQLS), 74 Capture–recapture methods, 169–170 Cataloging techniques, 94–95 Change scores, 127–130 pre-and post-parkland, 123–127 Clique percolation method (CPM), 55 Clique-based approaches, 55 Co-offending, 50–51 Coercive control, 79 Cohesion–delinquency relationship, 57 Collaborations, 187–188 between agencies and researchers, 169 Commercial sexual exploitation (CSE), 169 CSE/prostitution, 169 Community detection, 53–54 partnerships, 136 Community-based participatory research (CBPR), 136–138 challenges and consequences, 139–143 concentrated partner’s recruitment stage, 139 lessons learning, 143–145 process challenges, 139–142 process consequences, 142–143 research and battered immigrant women, 138 Comprehensive School Safety Initiative (CSSI), 116 Conflict, 71–72 gang, 58 Conflict Tactics Scale 2 (CTS2), 71 Crawlers (see Web-crawlers) Crime, 8, 11, 41–42, 118–119, 128 going up in society, 26 reporting patterns, 8 rise in, 23–26 survey, 71, 73 trends in, 22–23 victimization, 70 Crime measurement crime going up in society, 26 data and analysis plan, 13–14 future research implications, 27–28 homicide data, 18–20 overall crime trends, 14–16 police “outputs”, 20–22 police-recorded crime vs. estimates from victim surveys, 10–13 police-recorded crimes, 8–9 rise in crime or faulty wires, 23–26 trends in violence and crime, 22–23 trends in violence data, 17–18 Crime Survey of England and Wales (CSEW), 9 methodological issues with, 11–12 patterns of violence, 18 recent trends in, 13 as violent crime measurement, 27 Crime-recording practices, 8 systems, 14 Criminal anthropology, 1 assault, 71 behavior, 71 sciences, 1 statistics, 1–2 Criminal groups, 48 dynamics, 48–50 inter-group network dynamics, 58–59 intra-group network dynamics, 56–57 network approach to delineating group boundaries, 53–55 network data, 50–53 network perspective of delinquent groups, 49–50 research on criminal group dynamics, 56–59 Criminal justice, 102 administration, 1–2 FOI requests in criminal justice studies, 41 stakeholders, 171 system, 72 Criminology, 102 enterprise, 2 FOI requests in, 41–42 scholars, 53 Cyberstalking, 72 Data analysis techniques, 39 availability, 172–173 new data collected by agencies, 168 parsers (see Web-crawlers) scrapers (see Web-crawlers) sharing process, 172–173 source selection, 53 synthesizing, 174–175 types and format, 174 Delinquency, 48, 56 Dominance hierarchies concept, 58 Drug distribution networks, 59 Dynamic sex work environment, 86 Elder financial exploitation, 102–103, 108–109 age-graded life events on risk of, 109–110 chronology of study and methods, 105–108 consequences, 110–111 literature review, 102–105 protective factors against, 111 select findings from mixed methods approach, 108–111 Electronic devices, 119 Electronic wire-taps, 51 Emoticons, 189 Emotional neglect, 169 Ethnographic observations, 87–88 Existing data analysis, 166 for research purposes, 172 Exponential random graph models, 59 Extremism, 181–182, 185–188 Extremist sentiment online, 187 adaptation, 190 collaborations, 187–188 combinations, 188–189 validation, 189–190 Facebook, 52 Family violence, 169 Fear, 118–119, 122 Federal Trade Commission (FTC), 103 Feminist coalitions’ lobbying and education initiatives, 70 researchers, 73–74 scholars, 70 scholarship, 75 Field incident records, 51 Financial exploitation, 104 Forgery, 104 Freedom of information requests (FOI), 34 challenges, barriers, and tricks of trade, 37–40 requests in criminal justice studies, 41 requests in criminology, 41–42 requests in socio-legal studies, 40 in social sciences, 35–37 Funds, 104 Gang conflict, 58 Gang violence, 59 Gay/bisexual male community, 92 Global Positioning System, 52 Google, 93 Greater Manchester Police (GMP), 10 Grounded theory, 39 Group boundaries, network approach to delineating, 53–55 Group dynamics, 48–49 Happiness Index, 189 Her Majesty’s Inspectorate of Constabulary and Fire & Rescue Services (HMICFRS), 10, 16 High-quality client services, 141–142 Home Office Counting Rules (HOCR), 10 Homicide data, 18–20 Human trafficking, 166 challenges for using agency records in human trafficking research, 171–172 previous research using agency records, 169–172 Human Trafficking Reporting System (HTRS), 170 Immigrant-racialized women, 138 Immigration detention and deportation, 152 Immigration removal centers (IRCs), 152, 156, 161 Inter-group network dynamics, 58–59 Inter-university Consortium for Political and Social Research (ICPSR), 167 International CyberCrime Research Centre (ICCRC), 181 International Labour Organization (ILO), 170 International Lesbian, Gay, Bisexual, Trans, and Intersex Association (IGLA), 93–94 Internet, 86, 92–93 shopping behavior, 104 Interviewing male escorts (see also Male escort websites), 89 interview process, 91–92 university administrative issues, 90–91 working with sex workers’ community organizations, 91 Interviews, 87–88, 90 Intimate partner violence (IPV), 136 research requirements, 138–139 Intra-group network dynamics, 56–57 Islamic State (IS), 180 Jihadi material, 181 Las Vegas shooting terrorist attacks, 189 Law enforcement data, 105 Machine learning algorithms, 181, 187 Male escort websites (see also Interviewing male escorts), 92 accessing websites, 94 cataloging techniques, 94–95 Male independent escorting, 87, 90–91 internet-based escorting, 86 Male sex work research interviewing male escorts, 89–92 male escort websites, 92–95 methods, 86–89 Manchester attacks, 189 Media effects, 119 Methodology, 1 reflection, 172–175 techniques, 53 Minimizing underreporting, 72 multiple measures of victimization, 76–78 supplementary open-ended questions, 73–76 Mixed methods approach, 108 age-graded life events on risk of elder financial exploitation, 109–110 case study approach, 105–106 consequences of elder financial exploitation, 110–111 elder financial exploitation, 108–109 protective factors against elder financial exploitation, 111 reporting decisions of victims, 109 Multiple measures of victimization, 76–78 National Adult Protective Services Association (2016), 103 National Council on Aging (2017), 103 National Crime Recording Standard (NCRS), 10 National Crime Victimization survey (NCVS), 70, 76 National Crime Victimization Survey-Identity Theft Supplement (NCVS-ITS), 106–107 National Health and Medical Research Council, 90 National Health Services (NHS), 9 National Institute of Justice, 107 National Public Survey on White Collar Crime, 106–107 National White Collar Crime Center, 107 Nationalism, 181 Network approach to delineating group boundaries, 53–55 data, 50–53 methods, 3, 48 perspective of delinquent groups, 49–50 Off-line sexual assaults, 77 Offender–victim relationships, 50–51 “One victim, one crime” policy, 10 Opinion mining (see Sentiment analysis) Organized crime, 48 Pan-ethnic group categories, 78–79 PANAS-t, 189 Parkland characteristics, 120–121 Parkland effect, 126 Partner physical violence, 76 Pathways to Desistance study, 169 Photo-elicitation method, 155, 157 Photography, 154, 156 Photovoice, 152 challenges of using, 157–161 picturing life after detention, 153–157 Physical assault, 70–71 Physical health, 110–111 Physical neglect, 169 Police crime-recording practices, 9 Police records, 51 Police-recorded crimes, 8 in England and Wales, 8–9 methodological issues, 10–12 recent trends in, 13 victim surveys estimation, vs., 10–13 Polyvictimization, 77 Poverty, 70 Progressive legislative reforms, 70 “Prostitution” indicator, 169 Public agency data, 167 Quality of life, 110–111 Radicalization process, 187 Rape, 70–71 Reporting behavior, 108 Research failure, 152–153 Respect Inc, 90 Respondent-driven sampling, 52 Retirement community, 106, 110 Revenge pornography, 76 Revised Sexual Experiences Survey (RSES), 71 Risk assessment tools, 181 Risk factors, 105, 108 Royal Canadian Mounted Police (RCMP), 42 SASA, 189 School avoidance, 122 disorder, 122 safety research and programming, 116 School Crime Supplement to National Crime Victimization Survey School (NCVS-SCS), 119–120 School safety perceptions in aftermath of shooting characteristics of Parkland, 120–121 cross-sectional comparison in wave two, 123 data and methods, 121 differences in change scores pre-and post-parkland, 123–127 literature review, 118 measures, 122–123 media effects, 119 sample characteristics, 121 sample differences in examination of change scores, 127–130 shootings and student reactions, 119–120 violence and fear, 118–119 Self-report studies, 105 surveys, 70, 78, 103 Seniors vs Crime (SvC), 106 SenticNet, 189 Sentiment analysis, 181, 185–187 effectiveness, 188 with semi-parametric group-based modeling, 189 SentiStrength, 186–187, 189–190 SentiWordNet, 189 Sex education, 87 Sex trafficking sites, 95 Sex workers community organizations, 91 non-governmental organizations, 95 Sexual abuse, 169–170 assault, 71, 76 compulsivity, 87 harassment (see also Violence against women), 72, 76 intercourse, 71 Sexualization, 88–89, 92 Shootings and student reactions, 119–120 Social contagion process, 58 context, 88 desirability effects, 78 media, 52, 72, 87, 119 scientists, 187–188 social sciences, FOI in, 35–37 support services, 70 theories, 39 Social networks, 53 analysis, 42, 188 platforms, 52 Social Science and Humanities Council (SSHRC), 139 “Sociological stalking” implications, 159 Special weapons and tactics teams (SWAT teams), 41 Stakeholders, 171 Stalking, 70–72 Star rating system, 93–94 Stigma, 87 modes of, 89 Stop and search data analysis, 20–22 Street gang membership, 90–91 Surveys, 87–88 self-report, 70, 78, 103 victim surveys estimation, 10–13 Technology-facilitated coercive control, 80 Technology-facilitated stalking, 76–77 Temporal crime patterns, 9 Terrorism, 70, 185–188 The Dark Crawler (TDC), 182 keywords, 184–185 number of domains, 184 number of webpages, 184 trusted domains, 184 Traumatic behaviors, 73 Trusted domains, 184 Twitter, 52 Underreporting, 72 Unemployment, 70 University of Missouri–St Louis Comprehensive School Safety Initiative (UMSL CSSI), 117–118 US National Violence Against Women Survey (NVAWS), 70–71 Victimization, 71, 109, 116, 128 multiple measures of, 76–78 violence, 73 Victims, 102–103, 171 surveys estimation, 10–13 victim-oriented approach, 10 Victims of Trafficking and Violence Protection Act (2000), 169 Violence, 71–72, 118–119 data, 17–18 trends in, 22–23 victimization, 73 Violence against women, 69 broad operational definitions, 70–72 examining with-in group ethnic differences, 78–80 minimizing underreporting, 72–78 self-report surveys administered to adult men, 78 Violent crime measurement, CSEW as, 27 Visual criminology, 154–155 Web-crawlers, 182 WebSPHINX, 182 Win Web Crawler, 182 Book Chapters Prelims Introduction: Measuring Crime and Criminal Justice Part I: General Patterns and Trends Chapter 1: Is Crime Rising or Falling? A Comparison of Police-Recorded Crime and Victimization Surveys Chapter 2: Using Freedom of Information Requests in Socio-Legal Studies, Criminal Justice Studies, and Criminology Chapter 3: Criminal Group Dynamics and Network Methods Part II: Special Groups and Problems Chapter 4: Innovative Methods of Gathering Survey Data on Violence Against Women Chapter 5: Methods of Male Sex Work Research: Recommendations and Future Research Opportunities Chapter 6: Employing Mixed Methods: The Case of Elder Financial Exploitation Chapter 7: Perceptions of School Safety in the Aftermath of a Shooting: Challenge to Internal Validity? Part III: Crossing Boundaries Chapter 8: Methodological Challenges in Collaborative Research with Immigrant Women Experiencing Intimate Partner Violence in Canada Chapter 9: The Uses and Limits of Photovoice in Research on Life After Immigration Detention and Deportation Chapter 10: Agency Records as a Method for Examining Human Trafficking Chapter 11: Searching for Extremist Content Online Using the Dark Crawler and Sentiment Analysis Index
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,006 | 0,010 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,015 | 0,009 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,818 | 0,837 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».