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Record W2069052230 · doi:10.1080/14789940903174063

Harassment of Members of Parliament and the Legislative Assemblies in Canada by individuals believed to be mentally disordered

2009· article· en· W2069052230 on OpenAlexaffabout
Susan Adams, Tracey E. Hazelwood, Nancy L Pitre, Terry E. Bedard, Suzette D. Landry

Bibliographic record

VenueJournal of Forensic Psychiatry and Psychology · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsGovernment of CanadaAboriginal Affairs Northern Dev CanadaNortheast Cancer Centre
Fundersnot available
KeywordsHarassmentStalkingPsychologyParliamentLegislaturePopulationCriminologyPsychiatryMental healthPolitical scienceSocial psychologyMedicineLawPolitics

Abstract

fetched live from OpenAlex

Politicians may be more vulnerable to episodes of stalking than the general population, due to their public personas. Research indicates that perpetrators of such stalking episodes frequently suffer from a mental disorder. This study surveyed how often Canadian Federal and Provincial politicians, who held office in March 1998, had been harassed by individuals believed to be suffering from a mental disorder, as well as the form of the harassment. Four hundred and twenty-four politicians responded to the questionnaire (41.3%). Harassment was experienced by 29.9% of the respondents, with 87% believing their harassers to be suffering from a mental disorder. Both Federal and Provincial politicians were harassed, and overt threats against politicians were frequent. No association between threats in communications and subsequent physical approach was found. In fact, harassers who did not overtly threaten, or who both telephoned and wrote frequently, were shown to more often approach the politicians.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.025
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.309
Teacher spread0.295 · 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

Citations66
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Forensic Psychiatry and PsychologySame topicStalking, Cyberstalking, and HarassmentFrench-language works237,207