Bibliographic record
Abstract
It is well-documented that workers displaced from long-tenure jobs tend to have difficulty finding new employment, and face even greater difficulty finding a job without suffering a substantial loss in earnings. Workers with significant prior tenure typically undergo substantial earnings losses, with mean losses of 25-35 percent for those with at least five years’ tenure. Such earnings losses have been found to be persistent even five years after the displacement. Earnings losses suffered by displaced long-tenure workers tend to be large and may be permanent. Policies to address problems faced by displaced long-tenure workers tend to be centred on education, training and skill development. A report by CLSRN affiliate Stephen Jones (McMaster University) entitled “The Effectiveness of Training for Displaced Workers with Long Prior Job Tenure†(CLSRN Working Paper no. 92)* cautions that research shows returns to training for displaced workers that are low, being significantly less than the returns to formal schooling which lie in the 6-9% range. On a cost-benefit basis, the body of evidence does not show that training pays off for most of the displaced population. How does a firm’s decision to engage in employee training react to economic fluctuations? During downturns, lower productivity (a “negative productivity shock†) can be associated with increased training, as the opportunity cost to train workers is lower. However, increased productivity (a “positive productivity shock†) can be related to the adoption of new technologies that may require training, which can create increased return to skill. Currently, there is little evidence to prove which of the two scenarios holds more accurately over the other. In a paper entitled “The Impact of Aggregate and Sectoral Fluctuations on Training Decisions†(CLSRN Working Paper no. 45) CLSRN affiliates Vincenzo Caponi (Ryerson University), Cevat Burc Kayahan (Acadia University), and Miana Plesca (University of Guelph) examine how the firm-level decision to train depends on aggregate and sectoral output fluctuations, and find that more training tends to happen during downturns, and that training is generally higher in sectors that are doing relatively better than others.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.615 | 0.512 |
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".