Bibliographic record
Abstract
How does time off from work affect an individual’s job prospects? A study by CLSRN affiliates Kory Kroft (University of Toronto), Fabian Lange (McGill University) and Matthew J. Notowidigdo (University of Chicago) entitled “Duration Dependence and Labor Market Conditions: Theory and Evidence from a Field Experiment†(CLSRN Working Paper no. 101) examines the role of employer behavior in generating “negative duration dependence†– the adverse effect of a longer unemployment spell on individual reemployment prospects . The main finding is that the likelihood of getting a callback requesting an interview significantly decreases with the length of a worker’s unemployment spell prior to a job application. The labour market in the U.S., Canada, and many European countries has been characterized by dramatic structural changes in recent decades, partly due to technological change, globalization, and the shifting economic environment. In addition to these ongoing sources of adjustment, high unemployment rate and weak economic activities persist in many countries as they slowly recover from the “Great Recession†of 2008–09. Thus, whether unemployed workers are able to adjust efficiently to adverse employment shocks has become increasingly important for both individuals’ labour market success and the efficiency of the overall labour market. A paper by CLSRN affiliates Craig Riddell (University of British Columbia) and Xueda Song (York University) entitled “The Impact of Education on Unemployment Incidence and Re-employment Success: Evidence from the U.S. Labour Market†investigates the impact of formal education on transitions between labour force states, especially the transition from unemployment to employment. The study shows that education at both the secondary and post-secondary levels increases the probability of re-employment among the unemployed. Additional post-secondary education reduces the likelihood of becoming unemployed. Additional secondary education, however, does not have a significant influence on the likelihood of becoming unemployed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.479 | 0.348 |
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".