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Record W3041302612 · doi:10.1080/02701367.2020.1756198

An Analysis of Literature on Sport Officiating Research

2020· article· en· W3041302612 on OpenAlex
David J. Hancock, Samantha Bennett, Hannah Roaten, Kyle Chapman, Caleb R. Stanley

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch Quarterly for Exercise and Sport · 2020
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsStrengths and weaknessesDemographicsPsychologyRepresentation (politics)Applied psychologyData scienceComputer scienceSociologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Sport officials are crucial members of sport. Researchers have studied their roles numerous times, with results often informing sport procedures (e.g., athlete order in artistic sports). As the research on sport officiating spans five decades and several topics of interest, it is important that researchers periodically synthesize the literature. Purpose: The purpose of this study, therefore, was to conduct an analysis of literature on sport officiating research. Method: Guided by previous researchers, we executed four methodological steps including the article search, article retrieval, sample validity, and article coding. These steps yielded 386 articles for analysis, which ranged from 1971 to 2018. We coded the articles based on four main categories: article information, participant demographics, contextual information, and methodology. Results: Key findings from this analysis include a recent influx in sport officiating research, a vast number of publication journals, few studies dedicated to female-only participants, many studies missing relevant demographic information, an over-representation of interactors, and a reliance on quantitative studies. Conclusions: Though many researchers have conducted studies on sport officiating, several articles had poor methodological rigor (e.g., not reporting key demographic information). In the discussion and conclusion sections, we highlight strengths and weaknesses within the field and provide recommendations to guide future researchers and practitioners, to ensure robust research designs and guide applied practice.

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.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.437
Teacher spread0.365 · 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