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Record W4405228812 · doi:10.22456/1982-8918.143277

Sextortion in Sport

2024· article· en· W4405228812 on OpenAlexaff
Whitney Bragagnolo, Yaneí Lezama

Bibliographic record

VenueMovimento (Porto Alegre) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntellectual Property Law
Canadian institutionsWestern University
FundersUniverzita Karlova v Praze
KeywordsBusiness

Abstract

fetched live from OpenAlex

Sextortion, a form of sexual misconduct, entails the abuse of power to obtain sexual benefits, simultaneously rendering it an act of corruption. In sports, where power imbalances and hierarchical structures are common, sextortion is a growing issue. It often includes subtle or implicit threats, seeking compliance in exchange for privileges or opportunities. Despite growing awareness, research on sextortion in sports remains scarce. This study uses institutional theory and applied ethics to explore its key elements: the abuse of power and the improper obtention of sexual acts. The findings highlight the need for good governance, strong safeguarding protocols, and greater awareness of power dynamics, consent, and coercion. This research seeks to raise recognition among stakeholders and stresses the urgent need for prevention measures, guiding future policies and studies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.016
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.028
GPT teacher head0.330
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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