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Record W7000493607

An exploration of the developmental sport and training histories of Canadian sport officials

2019· dissertation· en· W7000493607 on OpenAlexaboutno aff

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2019
Typedissertation
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesSample (material)Variety (cybernetics)Sport managementTraining (meteorology)Youth sportsLarge sampleTeam sport
DOInot available

Abstract

fetched live from OpenAlex

Sport officials occupy essential roles in sport and are necessary for sport to function properly. However, compared to athletes and coaches there has been scant research conducted on the development of sport officials. Therefore, the purpose of this thesis was to explore the developmental pathways and milestones that might relate to success as an official. A sample of 223 Canadian sport officials completed The Developmental History of Officials Questionnaire, which collected information on sport and officiating participation histories, as well as training histories related to officiating. Results suggest that respondents??? highest level of athletic performance was predictive of a similarly high level as an official (H(3, n = 217) = 13.37, p < .01, ??2 = 0.06), thus past athletic participation might be beneficial for future officials??? development. Additionally, starting at a younger age as an official was also predictive of reaching a higher level as an official (F(3, 212) = 9.09, p < .001, ??2 = 0.90). Competitive officiating was the most relevant activity for skill development, with national/international level referees consistently officiating more hours throughout their career, while practice activities were not as prevalent. Future studies should attempt to increase the sample size, widen the variety of sports represented, and gather more respondents from lower- and middle-tier officiating backgrounds.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.035
GPT teacher head0.249
Teacher spread0.214 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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