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Record W4402250942 · doi:10.18060/28059

University Engagement with the Community through Physical Activity Opportunities

2024· article· en· W4402250942 on OpenAlexaff
David Michael Telles-Langdon, Nathan Hall

Bibliographic record

VenueMetropolitan Universities · 2024
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsBrock UniversityUniversity of Winnipeg
Fundersnot available
KeywordsCommunity engagementPhysical activitySociologyPsychologyEngineering ethicsPolitical sciencePublic relationsMedicineEngineeringPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

“Engaging marginalized members of the Community” became the mantra of the senior administration at the University as part of the appeal to various levels of government for financial support for university expansion to build the Health and RecPlex, a fieldhouse with fitness facilities, a gymnasium, several multipurpose rooms, and some research space. This research was an exploration of current university engagement with the community through sport and physical activity to understand how well a charter, to enshrine community access, addressed some of the issues related to marginalized groups living in the inner-city. Clarke’s Situational Analysis (2017) was used to deconstruct the interview transcripts, code, and develop themes, to put forth thick and rich descriptions of the outcomes from the implementation of the charter. Universities recognize they have a civic responsibility to engage and enrich the community in which they reside. This community engagement project was intended to address significant recreational needs within the community while also furthering academic initiatives. Some suggestions for potential improvement are highlighted.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0070.004
Open science0.0010.013
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.238
GPT teacher head0.461
Teacher spread0.223 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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