Deterring Player Holdouts: Who Should Do It, How to Do It, and Why It Has to be Done
Bibliographic record
Abstract
In the 1990s, major league professional team sports in North America will be remembered as a time of widespread player mobility, exponential player salary increases and incessant labor strife.These three characteristics triggered a legitimate threat to the integrity of professional sport, the player holdout.The holdout reflects the clash between the owner and his quest for long-term stability, against the players' mission to be paid their current market value.Owners will sign a marquee player to a long-term deal to please fans and promote team stability.However, certain players elect to try and coerce ownership into renegotiating existing contracts before the contractual term has expired.These players, usually perennial all-stars at the prime of their careers, will announce, likely during the off-season, that they will "hold out" from training camp and the upcoming season unless their contract is modified to reflect their "true value."'When negotiations reach a stalemate, the player will follow through on his threat and refuse to participate with the team.The holdout creates a media frenzy, upsets team chemistry, alienates fans and damages the league's reputation.Some holdout players return to action relatively quickly and cause only minimal disruption but others miss substantial parts of the season, or even the entire season.2 Alexei Yashin, star center of the Ottawa Senators of the National Hockey League (NHL), unhappy with his $3.6 million annual contract, -The author would like to thank Christine M. Harrington for her assistance in writing this article.1.This "true value" is what the player believes he is worth.2.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".