MétaCan
Menu
Back to cohort
Record W3087227232 · doi:10.1177/0886260520959645

Marketized Mentality, Street Codes and Violence

2020· article· en· W3087227232 on OpenAlexaffabout
Stephen W. Baron

Bibliographic record

VenueJournal of Interpersonal Violence · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsQueen's University
Fundersnot available
KeywordsDomestic violenceCriminologyPoison controlPsychologySuicide preventionHuman factors and ergonomicsViolent crimeSocial psychologyEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

The article examines the recent individual level extensions to Institutional Anomie Theory. It explores if a marketized mentality is important to the development of a violent street code that encourages violence as a method of self-enhancement, as well as a factor directly leading to violence. Further, it investigates if the impact of the marketized mentality on violence is moderated by risk-taking and violent peers. It controls for other important factors associated with violence including physical abuse, homelessness, violent victimization, and social bonds. The research utilizes self-report data from interviews with 400 Canadian homeless youth aged 16-24. Results from the OLS regressions indicated that a marketized mentality, along with risk-taking, violent peers, violent victimization, and social bonds predicted levels of support for the street code. The marketized mentality had a direct effect on violence, as well as an indirect effect through the street code. The effect of marketized mentality on violence was also stronger at higher levels of risk-taking and violent peer association. Physical abuse, violent victimization, risk-taking, and violent peers also had direct effects on violence. The findings suggest that a marketized mentality can be adopted even in economically marginal populations leading to the development of violent strategies to fulfill goals. Avenues for future research are offered.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.043
GPT teacher head0.347
Teacher spread0.304 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Interpersonal ViolenceSame topicCrime Patterns and InterventionsFrench-language works237,207