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Record W3135366326 · doi:10.1177/1461444821994490

Gangs and social media: A systematic literature review and an identification of future challenges, risks and recommendations

2021· article· en· W3135366326 on OpenAlex
Ariadna Fernández-Planells, Enrique Orduña‐Malea, Carles Feixa

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNew Media & Society · 2021
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
FundersH2020 European Research CouncilUniversity of Illinois at Urbana-ChampaignUniversité de MontréalHebrew University of JerusalemEuropean CommissionTemple UniversityWayne State UniversityBirmingham City UniversityUniversity of ConnecticutArizona State University
KeywordsScopusIdentification (biology)Social mediaSystematic reviewDiversity (politics)Content analysisSociologyPublic relationsPsychologyData scienceSocial sciencePolitical scienceComputer scienceMEDLINEWorld Wide Web

Abstract

fetched live from OpenAlex

Gang literature increasingly reflects the importance of social media in gang lifestyle, as gang members adopt new communicative practices. Yet, because of the multifaceted nature of online gang activity and the diversity of methodologies employed, a general overview of research outcomes is not easily achieved. This article seeks to remedy this by analysing academic studies of gang use of social media. A systematic literature review was conducted in Scopus and Google Scholar databases, which led to the identification of 73 publications. We then undertook a content analysis of each publication using an exhaustive evaluation model, comprising 20 variables and 71 categories. A bibliometric analysis was also performed to determine the structural characteristics of the research community that generates these publications. Our results point to an emerging universe of publications with different themes, methods, samples and ethical protocols. The challenges, risks and recommendations for future social media research with youth street groups are identified.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.263
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.310
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it