Taux de roulement et permanence de l’emploi dans l’industrie canadienne
Bibliographic record
Abstract
This study poses the question: "How long will the average new employee likely stay with his employer? This question has considerable relevance to the study of labour market activity, and to the obverse question: "How likely will a person, once employed, be unemployed again?" This paper explores the relevance of the tenure question on a number of fronts, and then develops a simple model for estimating the expected tenure of workers joining specific industries in Canada. Although the findings are based on somewhat dated statistics and lack a vector related to age, sex and other personal characteristics, they nonetheless confirm within reasonable degrees of confidence that the average new employee will remain with his employer a remarkably short time—less than two years in most industries and only a few months in some others. They suggest that employers are wise to defer costly training, pension and other non-wage expenditures until their new employees have built up some attachment to the firm. By the same token they affirm the usefulness of public income support programs to tide those who are laid off or quit through the transition to their next job, and for public retraining and mobility facilities to make the investments in human skills and allocation that employers will not.
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".