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Record W7097440458

Program Development IDENTIFIERS *Sport Management

2016· article· en· W7097440458 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsService (business)Professional developmentIdentifierPopulationProfessional associationAdministration (probate law)Service providerPolitics
DOInot available

Abstract

fetched live from OpenAlex

The field of sport and lifestyle management (SLM) includes three major eras which parallel cycles of conflict or stages of growth, development, and decline outlined in research. A review of relevant literature and examination of the situation in both Canada and the United States indicate that in the future SLM should return to a goal of service and provide a program that would enable it to realize full growth from an occupation to a profession. The stages included in Wilensky's "Chronological Life History of a Profession/Discipline " (1970) are related to DLM: (1) demonstrating that a substantial number of people are doing full-time activity; (2) establishing training schools; (3) founding professional associations; (4) conducting political lobbying; (5) developing codes of ethics; and (6) accrediting, certifying, and licensing. SLM managers can contribute as administrators, educators. and change agent researchers to service a broader target population in the areas of recreation, leisure, fitness, and athletic/sport organizations. The paper makes recommendations for the future of SLM based on a study by the American Assembly of Collegiate Schools of Business, SLM and physical and health education administration needs professionally trained, competent physical educators, with administrative expertise in theory and practice, who recognize the long-term benefits of gaining professional status r their occupation. (Contains 39 references.) (SM) ***********************************************;.**********i ' ****** Reproductions supplied by EDRS are the best that can be made * from the original document.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.562
Threshold uncertainty score0.802

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.007
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.4380.133

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 head0.031
GPT teacher head0.319
Teacher spread0.288 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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