Trade, Technology, and Transitions: Trampolines or Safety Nets for Displaced Workers?
Bibliographic record
Abstract
In the past several decades, the developed world has experienced significant labour market dislocations caused by international trade, technology, and other factors. While economic nationalism has risen in response to these challenges, technology is typically a more important factor than trade as a cause of these dislocations. Further, trade-related responses often impose additional costs on consumers through higher prices and on downstream industries that utilize inputs from protected sectors. Thus, the article argues that effective use of labour market adjustment policies (LMAPs) is a preferable approach to protectionist policies in addressing labour market adjustment costs. After laying out a spectrum of passive and active labour market policies, the article then goes on to provide a comparative evaluation of LMAPs in the USA, Canada, select Nordic and continental countries in Europe, and Australia. The article’s comparative evaluation suggests that the Nordic model, Germany, and Australia provide the most compelling utilization of LMAPs, while the USA lags behind other countries in our sample in relative resources devoted to LMAPs. However, recent trends in these jurisdictions suggest some degree of convergence on an ‘activation’ paradigm that utilizes incentive reinforcement and benefit conditionality in triggering participation in active labour market programmes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".