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Record W2770412355 · doi:10.1080/23802014.2017.1374208

Innovations in sport for development and peace research

2017· article· en· W2770412355 on OpenAlexafffund
Megan Chawansky, Lyndsay Hayhurst, Mary G. McDonald, Cathy van Ingen

Bibliographic record

VenueThird World Thematics A TWQ Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBrock UniversityYork University
FundersYork UniversityUniversity of GeorgiaBrock UniversityUniversity of KentuckyGeorgia Institute of Technology
KeywordsAtlantaScholarshipPluralPolitical scienceSociologyEngineering ethicsPublic relationsGeographyMetropolitan areaLawEngineering

Abstract

fetched live from OpenAlex

This collection emerged from the Innovations in Sport for Development and Peace (SDP) Research Symposium held in Atlanta, GA, in 2016. The contributors explore new terrain in seeking to further an innovative agenda on SDP within development discourses and practices. The authors provide insights from plural empirical and theoretical domains, including critical, feminist, post-colonial, and cultural studies perspectives. A central goal of this collection is to anticipate, inspire, and shape the next phase of research in, on, and about SDP. A further goal is to connect SDP and development scholarship.

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)0.011
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.011
Science and technology studies0.0070.010
Scholarly communication0.0110.006
Open science0.0010.010
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0130.001

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 head0.287
GPT teacher head0.488
Teacher spread0.201 · 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 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

Citations6
Published2017
Admission routes2
Has abstractyes

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