Resources over Constraints? Exploring Employer Branding in the Canadian Public Service from 1992 to 2024 Through BERT-Based Detection of Job Resources-Demands Dimensions
Bibliographic record
Abstract
In an era of intensified competition to attract and retain workers, employer branding has emerged as a strategic priority for public sector organizations. This article explores how the Canadian federal public service has shaped its employer image over time through official discourse. Drawing on 33 years of annual reports from the Clerk of the Privy Council (1992-2024), we examine how job demands and job resources are represented in institutional narratives. To do so, we develop and fine-tune a multi-label BERT-based classifier capable of detecting twelve psychosocial dimensions derived from the Job Demands-Resources (JD-R) model. Trained on over 30,000 annotated sentences from peer-reviewed academic literature, our classifier is applied at the sentence level to the whole corpus of Clerk’s reports, enabling a granular, diachronic mapping of the discourse on working conditions in Canada's federal public administration. The results reveal both a predominance of job resources over demands and temporal variations linked to major reforms and crises. Beyond contributing to the literature on public employer branding, our study demonstrates the potential of deep learning models for theory-driven content analysis in public management research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".