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Record W2919123014 · doi:10.1123/apaq.2018-0074

The Unheard Partner in Adapted Physical Activity Community Service Learning

2019· article· en· W2919123014 on OpenAlexaff
Rebecca T. Marsh Naturkach, Donna L. Goodwin

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReciprocalPsychologyService-learningFocus groupInterpretation (philosophy)Interpretative phenomenological analysisSocial psychologyDevelopmental psychologyPedagogyQualitative researchSociologyComputer science

Abstract

fetched live from OpenAlex

Community service learning (CSL), built on collaborative, reciprocal, and diverse disability-community partnerships, is a taken-for-granted pedagogical practice in adapted physical activity. Thus far, the CSL experiences of community members as they support student learning are virtually unknown. The purpose of the study was to understand how community members experienced an undergraduate adapted physical activity CSL course. Using an interpretative phenomenological analysis research approach, 9 adults (2 female, 7 male, mean age 50 years) experiencing disability participated in individual and focus-group interviews. Field notes and artifacts were also gathered. Relational ethics provided a heuristic framework to facilitate the interpretation of the findings. Four themes were crafted: (a) yes, we are willing partners; (b) but . . . we're in the dark; (c) subjected to being the subject; and (d) engage through relationships. Although overlooked as valuable collaborative and reciprocal partners, relational engagement remained central to the participants' CSL experience.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.009
Scholarly communication0.0050.004
Open science0.0010.011
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.043
GPT teacher head0.323
Teacher spread0.280 · 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 designQualitative
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

Citations9
Published2019
Admission routes1
Has abstractyes

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