Job Satisfaction in the Canadian Public Service: Mitigating Toxicity With Interests
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.
Bibliographic record
Abstract
During the 2015 Canadian federal election, political parties were polarized over the issue of job satisfaction in the public service. Critics and public service unions argued that there was a toxic environment under the leadership of Prime Minister Stephen Harper, and Liberal leader Justin Trudeau promised, if elected, to remedy this toxicity. Therefore, the job satisfaction of federal employees was a campaign promise of the now elected Liberals. Improving job satisfaction is not simple, as there are many competing factors impacting it. This study measures job satisfaction of Canadian public servants in 2014 and concludes that job satisfaction remained fairly high across the board, even under Stephen Harper, and that by far the strongest predictor of job satisfaction is how well employees’ interests match their job, followed by the relationship with their immediate supervisor, relationships with colleagues, and skills. Thus, human resource management policies are essential in improving job satisfaction.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it