Bibliographic record
Abstract
accessions estimated effects of EPL and entry costs 122, 123, 124, 125-7, 132 impact of dismissal costs 115-21 acyclical aggregate real wages 2, 37, 41, 45, 57 job reallocation 146 administrative burdens, business startups 111, 112 age entitlement to old-age pension benefits 68-9, 103 cross-country comparison 70-71 estimated effects of EPL and entry costs 122, 123, 124 retirement 66 agriculture, job flows, analysis 163 Amadeus database 149, 151 Australia real wage cyclicality CPI deflator 43 GDP deflator 42 labour market institutions 55 regression analysis 52 sample coverage 38 VAR-based results 48, 49 retirement decisions early retirement incentives 70, 73, 75, 77, 78 implicit taxes and labour market participation 81, 82 Austria job flows, analysis job creation and destruction 157, 158, 160, 163, 164, 166 sample description, firms and employment 151, 152, 154, 156 real wage cyclicality CPI deflator 43 GDP deflator 42 labour market institutions 55 regression analysis 52 sample coverage 38 VAR-based results 45, 48, 49 retirement decisions early retirement incentives 70, 72, 73, 77, 78, 79 implicit taxes and labour market participation 81, 82 Becker, Sascha O. 184-5 Belgium job flows, analysis job creation and destruction 157, 158, 160, 163, 164, 166 sample description, firms and employment 151, 152, 154, 156 real wage cyclicality CPI deflator 43 GDP deflator 42 labour market institutions 55 regression analysis 52 sample coverage 38 VAR-based results 45, 48, 49 retirement decisions early retirement incentives 70, 73, 77, 78 labour market participation effects of implicit taxes 81, 82 males aged 55-64 66 strictness of entry regulations 111 benefits, see pension benefits; unemployment benefits blue collars, estimated effects of EPL and entry costs 122, 123, 124 business services, job flows, analysis 163 business start-ups, administrative burdens 111, 112 Canada real wage cyclicality
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.706 | 0.588 |
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".