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Record W2105401120 · doi:10.1123/jsm.25.3.240

A Content Analysis of Environmental Sustainability Research in a Sport-Related Journal Sample

2011· article· en· W2105401120 on OpenAlexaff
Cheryl Mallen, Julie Stevens, Lorne J. Adams

Bibliographic record

VenueJournal of Sport Management · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBrock University
Fundersnot available
KeywordsContent analysisSample (material)SustainabilityPsychologySport managementSocial scienceSociologyPolitical sciencePublic relationsEcology

Abstract

fetched live from OpenAlex

This study systematically examined the extent of environmental sustainability (ES) research within the sport-related journal sample of academic literature to identify areas of under-emphasis and recommend directions for future research. The data collection and analysis followed a content analysis framework. The investigation involved a total of 21 sport-related academic journals that included 4,639 peer-reviewed articles published from 1987 to 2008. Findings indicated a paucity of sport-ES research articles (n = 17) during this time period. Further analysis compared the sport-ES studies within the sample to research in the broader management literature. A research agenda is suggested to advance sport-ES beyond the infancy stage.

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)0.019
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.092
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0340.028
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.000
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 head0.140
GPT teacher head0.373
Teacher spread0.232 · 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.

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

Citations81
Published2011
Admission routes1
Has abstractyes

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