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Record W2269283128 · doi:10.1080/00336297.2015.1117001

Interdisciplinary Best Practices for Adapted Physical Activity

2016· article· en· W2269283128 on OpenAlexaff
Rick Szostak

Bibliographic record

VenueQuest · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysical activityField (mathematics)Engineering ethicsDisciplineSection (typography)SociologyInterdisciplinarityPsychologyComputer scienceEngineeringSocial scienceMedicinePhysical therapy

Abstract

fetched live from OpenAlex

This article provides an introduction to the literature on interdisciplinary research. It then draws lessons from that literature for the field of adapted physical activity. It is argued that adapted physical activity should be a self-consciously interdisciplinary field. It should insist that research be performed according to recognized interdisciplinary and disciplinary methodologies. Each section of the article addresses an important aspect of the literature on interdisciplinary research and closes with a recommendation for adapted physical activity.

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)0.070
metaresearch head score (Gemma)0.086
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: Methods · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.086
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.005
Science and technology studies0.0050.010
Scholarly communication0.0150.010
Open science0.0070.018
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0090.003

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 head0.258
GPT teacher head0.535
Teacher spread0.278 · 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
GenreMethods

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

Citations12
Published2016
Admission routes1
Has abstractyes

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