JOURNAL OF SPORTS ECONOMICS / November 2000Lavoie / PAY DISCRIMINATI N I THE NHL Research Notes and Commentary The Location of Pay Discrimination in the National Hockey League
Bibliographic record
Abstract
Past studies have only uncovered a limited amount of evidence regarding salary discrimi-nation in the National Hockey League (NHL). Only French Canadian defensemen some-times seemed to be underpaid. It has been argued recently that the lack of evidence may be more a reflection of excessive aggregation than an absence of pay discrimination. In the present article, both national origin and the location of a player’s team are taken into account in salary regressions. The main outcome of the study is that salary discrimination based on team location appears to be a weak but pervasive phenomenon, more surely so in English Canada. An incidental outcome is that players located in English Canada cities were underpaid during the 1993-1994 season. Whereas past studies have uncovered substantial evidence pointing toward the existence of entry discrimination against French Canadians and possibly European players in the National Hockey League (NHL) (see Lavoie, in press), there is only limited evidence of salary discrimination, except perhaps at the position of defensemen (see Jones & Walsh, 1988; Lavoie & Grenier, 1992; McLean & Veall, 1992). In a more recent article, Neil Longley (1995) has challenged this received
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".