Flexibility and Fairness in Liberal Market Economies: The Comparative Impact of the Legal Environment and High‐Performance Work Systems
Bibliographic record
Abstract
Abstract This paper compares management flexibility in employment decision making in the United States and Canada through a cross‐national survey of organizations in representative jurisdictions in each country, Pennsylvania and Ontario, respectively, that investigates the impact of differences in their legal environments. The results indicate that, compared to their Ontario counterparts, organizations in Pennsylvania have a higher degree of flexibility in employment outcomes, such as higher dismissal and discipline rates, yet do not experience any greater flexibility or simplicity in management hiring and firing decisions. One explanation for this result may lie in the finding that organizations in Pennsylvania experience greater legal pressures on decision making, reflecting the generally more intense conflict in the employment law system in the United States. By contrast, high‐performance work systems, which some have looked to as a possible management‐driven mechanism for enhancing fairness in employment, had more modest effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".