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Record W4379144907 · doi:10.1080/10439463.2023.2218973

‘Not everybody can do this job’: a qualitative inquiry into emotional labour from RCMP detachment services assistants

2023· article· en· W4379144907 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

VenuePolicing & Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsMcMaster UniversityHamilton Health SciencesMemorial University of NewfoundlandWestern University
Fundersnot available
KeywordsEmotional laborTollEmotional stressPublic relationsQualitative researchPsychologyPolitical scienceSocial psychologySociologyMedicine

Abstract

fetched live from OpenAlex

Many police organisations employ and rely on public servants to complete specialised tasks with their organisations. The Royal Canadian Mounted Police (RCMP) regularly hires public servants known as Detachment Services Assistants (DSAs) to take on various support roles. As part of DSAs’ many clerical and administrative responsibilities, these workers must often perform emotional labour across different job tasks, which in turn, can be a personal yet occupationally mandated source of stress and strain. In the current study, we draw from semi-structured interviews with DSAs (n = 54) to investigate the different situations in which DSAs undertake emotional labour, the various styles of emotional labour DSAs perform, and the negative toll emotional labour places on DSAs in their workplace. Our research aims to contribute to the broader emotional labour literature on policing and the niche police literature on public servants, a form of civilian staff, employed by the RCMP.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
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
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.433
Teacher spread0.367 · 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