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Record W1543897645

Deterring Player Holdouts: Who Should Do It, How to Do It, and Why It Has to be Done

2001· article· en· W1543897645 on OpenAlexaboutno aff
Basil M. Loeb

Bibliographic record

VenueMarquette sports law review · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsAdvertisingBusiness
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.672
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.079
GPT teacher head0.330
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2001
Admission routes1
Has abstractyes

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