Perceived Causes of Career Plateau in the Public Service
Bibliographic record
Abstract
The purpose of this paper was to develop a better understanding of the causes of career plateau in the public service, focusing on 67 people who we determined to be career plateaued. Our interviews identified examples of incidents describing successes and interruptions in careers in developing an overall picture of the reasons for people being plateaued. We identified ten themes, which were grouped into three areas: deficiencies in experience, skills and education (four themes); competition skills (four themes); and perceptions of favoritism and discrimination (two themes). In addition to feeling plateaued because of the inability to demonstrate experience, education, and knowledge, many people offered examples of being plateaued because of the lack of interviewing skills or evidence of favoritism and discrimination. Those who are plateaued because of favoritism or discrimination verbalize feelings of disgust and frustration and illustrate a tendency to become less engaged with their work. We think that the negative impacts of favoritism or systemic discrimination have important implications because they are likely to have an impact on employees and their engagement in their work and life. However, as our results are based a sample of 67 government employees in the Canadian public service, they require verification in other settings.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".