MétaCan
Menu
Back to cohort
Record W2586558726 · doi:10.1123/cssm.2015-0062

Selecting Sport Events to Serve Public Policy Agendas

2016· article· en· W2586558726 on OpenAlex
Marijke Taks, Laura Misener

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCase Studies in Sport Management · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsWestern UniversityUniversity of Windsor
Fundersnot available
KeywordsTourismOfficerEvent (particle physics)Public relationsSet (abstract data type)AthletesInterpretation (philosophy)Identification (biology)Political sciencePublic policyMarketingBusinessMedicine

Abstract

fetched live from OpenAlex

In this case, a local sport tourism officer has been asked to prepare a recommendation for Evex City Council regarding which types of events the city should bid for, based on their public policy agenda of enhancing tourism for economic development purposes and stimulating sport participation for residents. A questionnaire, a codebook, and a data set from two events, an international figure skating event and a provincial gymnastics event, are provided to assist in making a decision. The data set includes the spectators’ identification with and motives for attending the events, tourism activities in which they participated, and some sociodemographic variables. Analyses of the data and interpretation of the results should assist the sport tourism officer in providing accurate recommendations to policymakers. Theories and frameworks that underpin this case include public policy schemas; identity, motives, and tourism behavior of event attendees; sport participation outcomes from sport events; leveraging; and event portfolios.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.101
GPT teacher head0.415
Teacher spread0.314 · 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