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Record W2767968389 · doi:10.1123/jsm.2017-0197

The National Collegiate Athletic Association as a Social-Control Agent: Addressing Misconduct Through Organizational Layering

2017· article· en· W2767968389 on OpenAlexaff
Khirey Walker, Chad Seifried, Brian P. Soebbing

Bibliographic record

VenueJournal of Sport Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Alberta
FundersNational Collegiate Athletic Association
KeywordsMisconductWrongdoingSanctionsAssociation (psychology)Control (management)Social controlPublic relationsPsychologyPolitical scienceSocial psychologyLawManagementEconomics

Abstract

fetched live from OpenAlex

The present study focuses on the National Collegiate Athletic Association and cases of misconduct from 1953 to 2016 to examine evidence of organizational layering created by social-control agents. The historical method was employed and found wrongdoing may influence the creation of organizational layers to control and/or manage future behavior. Furthermore, the activities of the National Collegiate Athletic Association featured variation in centralization, formalization, and complexity through expanding horizontal; vertical (e.g., institutional, managerial, and technical); and spatial differentiations. Second, individual social-control agents impact future organizational policies and member behavior but social-control agents’ power may be challenged as an organization grows. Third, as a social-control agent, the National Collegiate Athletic Association struggled with assessing cases of misconduct, assigning sanctions in a timely manner and at a level to deter future wrongdoing. Finally, the present study offers several propositions connecting third-party regulators to the synergy between complexity (i.e., horizontal and vertical differentiations); formalization; and centralization.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.001
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 head0.052
GPT teacher head0.350
Teacher spread0.298 · 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 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

Citations13
Published2017
Admission routes1
Has abstractyes

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