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Record W2782840194 · doi:10.15203/ciss_2017.011

Sport officiating recruitment, development, and retention: A call to action

2017· article· en· W2782840194 on OpenAlex
Lori A. Livingston, Susan L. Forbes, Nick Wattie, N. G. Pearson, Tony Camacho, Paul Varian

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCurrent Issues in Sport Science (CISS) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPublic relationsCall to actionAction (physics)Political scienceGovernment (linguistics)Statement (logic)Sport managementProfessional sportPsychologyPublic administrationMarketingBusinessLaw

Abstract

fetched live from OpenAlex

The purpose of this article is to report on the outcome of a two-day consensus-building exercise amongst sport scientists and sport practitioners interested in the recruitment, development, and retention of sport officials. Twenty participants including volunteers and paid employees affiliated with nine Ontario-based sport organizations, university researchers, and provincial government policy makers participated. A consensus statement regarding this aspect of sport officiating and, more specifically, “What do we know?”, “What don’t we know?”, and “Where does the research need to go from here?” is presented. A willingness to consider and embrace these ideas may be critical in moving sport officiating from being an understudied and undervalued segment of the sport system to receiving the attention and respect it deserves going forward.

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)0.001
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 score0.697
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.268
GPT teacher head0.479
Teacher spread0.212 · 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