MétaCan
Menu
Back to cohort
Record W2156712209

Female stalkers and their victims.

2003· article· en· W2156712209 on OpenAlexaboutno aff
J. Reid Meloy, Cynthia Boyd

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsnot available
Fundersnot available
KeywordsStalkingPsychologyJealousyAngerLonelinessPoison controlAbandonment (legal)Suicide preventionPsychiatryClinical psychologyInjury preventionBorderline personality disorderSocial psychologyMedicineMedical emergency
DOInot available

Abstract

fetched live from OpenAlex

Demographic, clinical, and forensic data were gathered in an archival study of 82 female stalkers from the United States, Canada, and Australia. Female stalkers were predominantly single, heterosexual, educated individuals in their mid 30s who had pursued their victims for more than a year. Major mental disorder and personality disorder were suggested, especially borderline personality disorder. They usually threatened violence, and if they did threaten, were more likely to be violent. Frequency of interpersonal violence was 25 percent, but there was limited use of weapons, and injuries were minor. Stalking victims were most likely to be slightly older male acquaintances; but if the victim was a prior sexual intimate of the female stalker, her risk of being violent toward him exceeded 50 percent. Unlike male stalkers who often pursue their victims to restore intimacy, these female stalkers often pursued their victims to establish intimacy. Common emotions and motivations included anger, obsessional thoughts, rage at abandonment, loneliness, dependency, jealousy, and perceived betrayal. Results are interpreted from a clinical and risk management perspective.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.250
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations95
Published2003
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicStalking, Cyberstalking, and HarassmentFrench-language works237,207