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Record W2163840770 · doi:10.1037/tam0000016

Threats, approach behavior, and violent recidivism among offenders who harass Canadian justice officials.

2014· article· en· W2163840770 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Threat Assessment and Management · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsWaypoint Centre for Mental Health CareGovernment of Ontario
Fundersnot available
KeywordsRecidivismCriminologyEconomic JusticePsychologyCriminal justicePolitical scienceLaw

Abstract

fetched live from OpenAlex

We examined the characteristics of offenders who harassed justice officials, comparing those who threatened or approached their victim with those who engaged in other problematic communications. We also explored predictors of subsequent violence. We identified 86 offenders from the files of a justice officials protection and investigation service in Ontario, Canada, who had used threatening, disturbing, intimidating, or harassing language (written or verbal) toward police, prosecutors, judges, defense attorneys, probation officers, or correctional workers. We conducted chi-squared tests and ANOVAs to compare offenders who did versus did not threaten or approach on criminal history, substance abuse, mental health, and other variables at the index offense, and tested predictors of future violence using the receiver operating characteristic (ROC) area under the curve. Using threats was associated with being male, a prior criminal history, substance abuse, and suicidality. Approaching the victim was associated with younger age, less previous offending, and absence of a prior acquaintance with the target. Postindex criminal offending was common (55%), but typically nonviolent, and on only 3 occasions (4%) was the victim the original target of harassment. When violent recidivism did occur it was not toward the target; it was best predicted by younger age at index, criminal history, and using threats. Offenders who harass justice officials are rarely violent toward these victims, and their violence is predicted by well-established variables.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.326
Teacher spread0.296 · 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