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Record W3106752814 · doi:10.1080/01490400.2020.1830897

Examining Self-Other Constructions to Advance the Social Justice Goals of Leisure Research

2020· article· en· W3106752814 on OpenAlexaff
Colleen Reid, Ania Landy, Marina Morrow, Maggie Bosse

Bibliographic record

VenueLeisure Sciences · 2020
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsVancouver Coastal HealthYork UniversityUniversity of British ColumbiaDouglas College
Fundersnot available
KeywordsParticipatory action researchCommunity-based participatory researchSociologyCitizen journalismPublic relationsPower (physics)Social justiceSocial psychologyPsychologyCriminologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Community-based participatory research (CBPR) is used increasingly in leisure research to foster equitable relationships and social change, yet individuals who disseminate CBPR remain fraught with the challenges of upholding CBPR’s central values and principles in the daily practices of CBPR. In this analysis we examine the promises and challenges of integrating mental health ‘peer’ research participants into all phases of the CBPR process. While peers’ experiences were largely positive, reflections from team members revealed ongoing tensions in attending to power differences between academic researchers and peers - what the research team called “self-other” constructions. Our efforts to unsettle ‘self-other’ constructions built peers’ capacity and personal growth but perpetuated role distinctions, power inequalities, and tensions around structure and control. These tensions are highly instructive for leisure researchers “to reimagine leisure studies and its role in helping society understand, confront, and address complex social challenges” (Glover, 2015 Glover, T. D. (2015). Leisure research for social impact. Journal of Leisure Research, 47(1), 1–14. https://doi.org/10.1080/00222216.2015.11950348[Taylor & Francis Online], [Web of Science ®] , [Google Scholar], p. 1).

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.179
GPT teacher head0.450
Teacher spread0.271 · 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 teacher head, 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

Citations1
Published2020
Admission routes1
Has abstractyes

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