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Record W2039423841 · doi:10.1177/1073191106288180

Harm, Intent, and the Nature of Aggressive Behavior

2006· article· en· W2039423841 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAssessment · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthWestern University
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsAggressionHarmPsychologyHuman factors and ergonomicsPoison controlClinical psychologyInjury preventionSocial psychologyDevelopmental psychologyMedical emergencyMedicine

Abstract

fetched live from OpenAlex

The research goals were to use the constructs of harm and intent to quantify the severity of aggression in the real-world setting of the bar/club, to describe the range of aggressive behaviors and their relationship to harm and intent, and to examine gender differences in the form and severity of aggression. Systematic observations were conducted by trained observers on 1,334 nights in 118 bars/clubs. Observers documented a range of aggressive acts by 1,754 patrons in 1,052 incidents, with many forms of aggression occurring at more than one harm and intent level. Women used different forms of aggression, inflicted less harm, and were more likely to have defensive intent compared with men. Implications of the findings for research and measurement of aggression and applications to preventing aggression and violence are discussed.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.756

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 studies0.0000.001
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 head0.010
GPT teacher head0.344
Teacher spread0.333 · 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