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Record W4403262524 · doi:10.1080/14927713.2024.2411427

(Re)creation through recreation: strengthening Hul’qumi’num’ identities through ‘leisure’

2024· article· en· W4403262524 on OpenAlexafffundvenue
Britta Peterson

Bibliographic record

VenueLeisure/Loisir · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRecreationSociologyLeisure timeHumanitiesPolitical scienceArtPhysical activity

Abstract

fetched live from OpenAlex

A strong understanding of one’s cultural identity can serve as a protective factor against adversity. Correspondingly, empirical evidence has identified that participation in leisure may be a catalyst or contributing factor to forming one’s identity. However, scholarly understandings of the term leisure have been dominated by Eurocentric ideals. In this paper, I used Hul’qumi’num’ snuw’uy’ulh (sacred teachings) as my theoretical framework to explore how Hul’qumi’num’ mustimuxw (Hul’qumi’num’ people), specifically participating members of Snuneymuxw First Nation, understand and engage in leisure practices. Resultantly, I present my reflexive and tension filled process to assert that while study participants differentiated between engagement in both Indigenous and non-Indigenous leisure pursuits, the overarching premise was that one’s cultural identity and leisure participation were not independent of one another, regardless of the nature of the activity. Moreover, I suggest that in contemporary contexts, leisure participation from the perspective of the participants did not distinguish between Indigenous and non-Indigenous leisure to create either/or binaries but rather presented a notion of both. Thus, I demonstrate the reciprocal utility of Indigenous and non-Indigenous leisure pursuits in developing and maintaining cultural identity for Hul’qumi’num’ mustimuxw when rooted in snuw’uy’ulh.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
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 head0.041
GPT teacher head0.340
Teacher spread0.299 · 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.

Study designTheoretical or conceptual
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

Citations3
Published2024
Admission routes3
Has abstractyes

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