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Record W2808633324

Cold Careers and Occupational Hazards: The Occupational Preferences of Canadian Serial Killers

2018· article· en· W2808633324 on OpenAlexaboutno aff
Christina E. Ledezma

Bibliographic record

VenueScholarWorks - GVSU (Grand Valley State University) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthPsychologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Serial killing is a dark and complex phenomenon. As researchers have begun to recognize that serial killing exists and interacts within a broad modern context, how these factors affect its occurrence has received more attention. This includes serial killers’ occupational preferences and the influence that occupations have on their offending. However, studies on serial killers’ occupational preferences have been limited to the United States and the United Kingdom. This thesis sought to classify the occupational preferences of 36 Canadian serial killers and subsequently analyze how these occupations may have influenced their offending, both instrumentally and psychologically. According to Canada’s 2016 National Occupational Classification, Canadian serial killers preferred occupations in “Management occupations,” “Sales and services occupations,” and “Trades, transports and equipment operators and related occupations.” Using content analysis on biographical cases of Canadian serial killers, it was proposed that these work environments were the most preferred since they contained occupational elements advantageous for their offending. Specifically, the freedom of movements—typically through a vehicle—the lack of supervision, and the provision of solitude. Hence, Canadian serial killers’ offending was shown to be influenced by a lesser-known contemporary lifestyle factor: occupation. This thesis adds to the greatly under-developed literature on serial killers’ occupational preferences and encourages further exploration for both research and application.

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 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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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