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Differentiating between Physically Violent and Nonviolent Stalkers: An Examination of Canadian Cases

2008· article· en· W2018518393 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 Forensic Sciences · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsUniversity of Sudbury
Fundersnot available
KeywordsPoison controlInjury preventionHuman factors and ergonomicsOccupational safety and healthSuicide preventionPsychologyMedical emergencyCriminologyForensic engineeringMedicineEngineering

Abstract

fetched live from OpenAlex

This study is one of a few that empirically investigated factors that differentiated the physically violent stalker from the nonviolent stalker. Using discriminant analysis, 103 Canadian cases of "simple obsessional" stalking were examined. Overall, the success of the model for classifying cases into one of two groups was 81%. Results revealed that the physically violent stalker is more likely to: (a) have a stronger previous emotional attachment toward their victim; (b) be more highly fixated/obsessed with their victim; (c) have a higher degree of perceived negative affect towards their victim; (d) engage in more verbal threats toward the victim; and (e) have a history of battering/domestic abuse towards the victim. Overall, the variables that best differentiate the physically violent stalker from the nonviolent one appear to characterize underlying themes of anger, vengeance, emotional arousal, humiliation, projection of blame, and insecure attachment pathology.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.322
Teacher spread0.241 · 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