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Record W4393364954 · doi:10.1111/capa.12557

Frontiers of public service motivation research in Canada: A scoping review

2024· review· en· W4393364954 on OpenAlexafffundabout
Wiesława Dominika Wranik, Michelle McPherson, Isabelle Caron, Huiyan Liu

Bibliographic record

VenueCanadian Public Administration · 2024
Typereview
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of British ColumbiaDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPublic service motivationNature versus nurturePublic sectorContext (archaeology)Public relationsPublic serviceWork (physics)Job satisfactionPolitical sciencePublic administrationBusinessPsychologySociologyEngineeringSocial psychologyGeography

Abstract

fetched live from OpenAlex

Abstract The Canadian public sector employs around one‐fifth of Canadian workers; they are responsible for policy making, stewarding public funds, and serving the public. Canadian governments struggle with job satisfaction, engagement, retention, and turnover, all of which may be associated with public service motivation (PSM). We conducted a scoping review of Canadian PSM research to synthesize what is known about these associations in the Canadian context. We identified 24 published studies and four works in progress. These demonstrate that PSM exists among Canadian public servants from the early stages of their career until later stages, and that PSM bolsters attraction to public sector work among students. No studies measured PSM among Canadian public servants using a validated instrument. Further research about the contributions of PSM to the quality of Canadian public services and how employers can nurture PSM is recommended, particularly given recent changes in work environments.

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)0.018
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0270.042
Science and technology studies0.0030.003
Scholarly communication0.0070.002
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.413
GPT teacher head0.502
Teacher spread0.088 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes3
Has abstractyes

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