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Record W4353085198 · doi:10.5539/jpl.v16n2p1

Analyzing Pre-Contracts Agreement in Professional Footballer Contracts in Saudi Arabia: Can Players Change Their Minds?

2023· article· en· W4353085198 on OpenAlexvenueno aff
Ammar Alrefaei

Bibliographic record

VenueJournal of Politics and Law · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsFootballNegotiationSalaryGood faithArbitrationBiddingFlexibility (engineering)BusinessLeagueLawLaw and economicsCrippleEconomicsPolitical scienceMarketingManagement

Abstract

fetched live from OpenAlex

FIFA's (The Fédération Internationale de Football Association) regulation and Saudi professional player's regulation allow footballers and their agents to start negotiating new deals with third parties in the last six months of the contracts. Some players and agents may use this period to initiate bidding wars between rival clubs by entering pre-contracts with an alleged possibility of terminating them at no cost. There is growing evidence of such practices in Saudi Arabia after revoking the salary cap rule in professional football contracts. This article analyses this issue through the lens of the existing legal treatment of pre-contracts by FIFA, CAS (Court of Arbitration for Sport), and applicable national laws (Swiss and English). Based on the findings, a series of propositions are made to introduce into the future regulations governing Saudi football leagues. The goal of such propositions is to avoid misapplication of pre-contracts, maintain contractual stability, encourage negotiations in good faith, and promote competitiveness without reducing contractual flexibility for players in the wake of salary cap cancellation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

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

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 head0.047
GPT teacher head0.263
Teacher spread0.215 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2023
Admission routes1
Has abstractyes

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