Job Insecurity in a Sample of Canadian Civil Servants as a Function of Personality and Perceived Job Characteristics
Bibliographic record
Abstract
Economic downturns and organisational changes have stimulated studies on the importance of job security for public employees; however, there has been some disagreement as to whether job insecurity should be defined using a single- or a two-factor model, on how to measure it and which antecedents and consequences are linked to it. Questionnaires measuring job insecurity as well as personality traits and job characteristics as possible antecedent variables, and job performance and job satisfaction as possible consequences were completed by a sample of 71 federal government employees during recent important government downsizing. A multiple regression analysis indicated that job insecurity, defined as a combination of organisational or personal vulnerability, was significantly related to only two antecedent variables, i.e., Neuroticism and job characteristics. Further regression analyses indicated that job insecurity was a statistically significant moderator but only between one consequence, i.e., intention to quit, and the job characteristics variable. These results along with methodological issues and further research, are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".