MétaCan
Menu
Back to cohort
Record W2810095861 · doi:10.3390/su10072241

Sport for Development and Peace and the Environment: The Case for Policy, Practice, and Research

2018· article· en· W2810095861 on OpenAlexaff
Richard Giulianotti, Simon C. Darnell, Holly Collison, P. David Howe

Bibliographic record

VenueSustainability · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Toronto
FundersEconomic and Social Research Council
KeywordsPolitical scienceEngineering ethicsEnvironmental planningPublic administrationEnvironmental ethicsPublic relationsProcess managementBusinessEngineeringGeographyPhilosophy

Abstract

fetched live from OpenAlex

This paper highlights the need for critical attention and reflection within the Sport for Development and Peace (SDP) sector regarding the physical environment. Drawing on fieldwork that examined a variety of SDP initiatives in five different countries, we argue that instrumental concerns at local levels often mean that the physical environment takes a back seat to other development priorities within SDP activity. This is despite the critical importance of issues, such as environmental degradation and the threats posed by climate change, as well as the fact that sport is directly linked to the United Nations’ Sustainable Development Goals and the 2030 Agenda. After providing examples of the relegation of the physical environment in different SDP contexts, we put forth three conceptual frameworks that would be useful within SDP scholarship for advancing critical discussion on this issue within the sector. The final section discusses both the implications of these initial findings and suggests questions and topics for future research around this timely issue.

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)0.011
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.436
Teacher spread0.382 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations45
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicSport and Mega-Event ImpactsFrench-language works237,207