29 Just Labour: A Canadian Journal of Work and Society – V.15 – Special Edition – Nov. 09 FURTHER TESTS OF THE LINK BETWEEN UNIONIZATION, UNEMPLOYMENT, AND EMPLOYMENT: FINDINGS FROM CANADIAN NATIONAL AND PROVINCIAL DATA
Bibliographic record
Abstract
There is a substantial international economics literature regarding the impact of labour market and social institutions (such as trade unions and collective bargaining systems) on labour market performance (measured by indicators such as unemployment rates and job-creation). Some recent installments in that literature include OECD (2006), Howell (2005), and Hein, Heise and Truger (2006). The broad finding of this international research is that there is no predictable relationship either way between trade unionization, unemployment rates, and employment levels. Countries with stronger or weaker unions and collective bargaining regimes may experience stronger or weaker labour market outcomes, depending on the other, more important economic and structural factors that affect labour markets (such as macroeconomic conditions and demographics). Despite these international findings, certain opponents of proposed changes to U.S. labour law (and, in particular, the Employee Free Choice Act) have attempted to argue that Canada’s labour market experience “proves ” that
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.010 |
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".