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Record W3093646636 · doi:10.1123/jsm.2020-0016

An Analysis of Training-Related Outcomes Within Canadian National Sport Organizations

2020· article· en· W3093646636 on OpenAlexaffabout
Patti Millar, Julie Stevens

Bibliographic record

VenueJournal of Sport Management · 2020
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsBrock UniversityUniversity of Windsor
Fundersnot available
KeywordsTraining (meteorology)Context (archaeology)PsychologyInterpretation (philosophy)Public relationsResource (disambiguation)Applied psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Past research has demonstrated that human resource training often results in improved individual and organizational performances. Yet, the focus has been on whether or not training has an impact on performance, rather than the nature of that impact. The purpose of this study is to investigate the nature of training-related outcomes in the context of one training program within the Canadian national sport sector. Interviews were conducted with key representatives from 12 Canadian national sport organizations. Findings showed the manifestations of performance change that occur as a result of training, revealing a new way of thinking at the individual level, a new way of doing within group and organizational processes, and a new way of being across organizations. Three theoretical perspectives—interpretation, learning, and institutional—are used to frame the discussion of the findings. Implications for practice and future research are presented.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0080.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.320
Teacher spread0.282 · 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 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

Citations2
Published2020
Admission routes2
Has abstractyes

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