MétaCan
Menu
Back to cohort
Record W2739308768 · doi:10.1177/1461355717717996

‘We deal with human beings’

2017· article· en· W2739308768 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

VenueInternational Journal of Police Science & Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsWestern University
Fundersnot available
KeywordsEmotional laborAffect (linguistics)Work (physics)PsychologySocial psychologyRelation (database)Qualitative researchPublic relationsCriminologyApplied psychologySociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Dealing with emotions is a central feature of everyday police work. This is especially the case in relation to criminal investigation work, in which police investigators must grapple with both their own emotions and those of the victims and families with whom they deal. Despite the importance of emotional labor in understanding criminal investigation work, this aspect of their work remains understudied. This study is based on data from 13 in-depth qualitative interviews with members of the Canadian police services. Within it, we explore how officers engage in emotional labor, as well as its impact on these individuals. Although our results are preliminary in nature, they do reveal how managing emotions according to organizationally sanctioned display rules can affect officers’ well-being, and highlight the need for future research to enable police organizations to deal more effectively with this form of work-related stress.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0010.002
Open science0.0040.000
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 head0.038
GPT teacher head0.429
Teacher spread0.391 · 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