A convergence analysis on the efficiency of public job placement services in Japan
Bibliographic record
Abstract
Public job placement services assist in “pairing together” job offers with job seekers. The aim is to mass-produce high quality jobs meeting the requirements of both parties and to help the “underdogs in the employment market” by bearing the “pairing costs,” or job-searching costs of both parties. The provision of unemployment benefits is a passive policy in the labor market, while public job placement services are typically an active policy, since the government is actively involved in the labor market. Public job placement services hold a particularly critical position among Japan’s employment policies. As shown in Table 1, over 60% of expenditures for active employment measures are allocated to public job placement services in Japan. In European countries, apart from the UK, these services account for less than 20%, and in the US, the ratio is less than 30%. Unlike Canada, Denmark, Germany and the US, where the government places greater emphasis
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".