Analyzing Job Mobility with Job Turnover Intentions: An International Comparative Study
Bibliographic record
Abstract
This study analyzed job-turnover intentions in twenty-five countries with data from the 1997 International Social Survey Program. Results show that high turnover intentions, measured as the proportion of employed individuals that report a very likely change of job in the next 12 months, can be observed in Canada, 17.0 percent, the U.S., 14.3 percent, and Great Britain, 14.3 percent. Low turnover intentions are encountered in Japan, 1.8 percent, Spain, 3.0 percent, and several Eastern European countries. The determinants of turnover intentions are, in a number of cases, the same as those observed in many studies on actual turnovers. Determinants of turnover intentions do vary substantially among countries. However, union membership, public-sector employment, job satisfaction, job security, and firm pride are significant in most countries. The high turnover intentions observed in Great Britain, France, and, to a lesser extent, Canada can be explained by the subjective measures considered in this study. Especially Great Britain and France have very low rankings of job satisfaction, job security, advancement opportunities, and firm pride. Despite having the same turnover intentions, Great Britain and the United States differ substantially with regard to the subjective determinants. Whereas the high turnover intentions in Great Britain can be explained by the relatively poor perceived job satisfaction, job security, and firm pride, high turnover intentions in the United States coexist with relatively high levels of job satisfaction, job security, advancement opportunities, firm pride, and, most notably, good perceived labor market opportunities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".