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Record W4417051411 · doi:10.1080/2201473x.2025.2594504

A scoping review exploring the relationships between Indigenous peoples and parks and reserves: Just more colonial structures

2025· article· en· W4417051411 on OpenAlexaffabout
Els Russell, Robin Quigg, H. Robson MacDonald

Bibliographic record

VenueSettler Colonial Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsCarleton University
Fundersnot available
KeywordsIndigenousColonialismIdentity (music)EthnographyDecolonization

Abstract

fetched live from OpenAlex

Parks and reserves are geographically demarcated and socially constructed. They are often celebrated as public places that are accessible to all. But are they places that honour and respect Indigenous values, especially those in more urban settings, managed by local and provincial governments? In Aotearoa New Zealand, legislation enshrines the involvement of Indigenous Māori in the management of these defined spaces, but particularly in the non-national park environments, historically, these have often been places of disenfranchisement, alienation, and removal. This scoping review explores how Indigenous peoples engage with state and local parks and reserves across four settler colonial countries (Aotearoa New Zealand, Australia, the United States of America, and Canada). After screening more than 5,000 articles, the analysis of 102 articles revealed three core themes: (1) a dominance of binary depictions of nature and culture; (2) evidence that conservation is used as a colonial tool; (3) that existing and historical policy depict the dominance of Western policy frameworks and the scale of legislative control. By mapping the current field of research, this review demonstrates that, for the non-national park context, regulatory and legislative requirements alone are often insufficient to ensure parks and reserves depart from their histories of Indigenous disenfranchisement.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0160.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.118
GPT teacher head0.402
Teacher spread0.283 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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