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Record W4296824226 · doi:10.5070/p538358970

Recent protected area–Indigenous Peoples research articles in Canada and the USA: A meta-review

2022· article· en· W4296824226 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueParks Stewardship Forum · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousScope (computer science)DisciplinePolitical scienceMeta-analysisSystematic reviewLibrary scienceGeographySociologySocial scienceMEDLINELawMedicineEcology

Abstract

fetched live from OpenAlex

We conducted a meta-review of 66 peer-reviewed articles published between 2008–2020 concerning Indigenous Peoples’ interactions with protected areas in the United States of America and Canada. Our meta-analysis centered on characterizing this literature’s response to the concerns of critical Indigenous studies by examining the topical, geographic, and disciplinary scope of the literature, as well as authors’ backgrounds and the journals where research is published. We additionally considered the presence of Indigenous persons as authors and participants. We found the literature is published widely, across many journals and disciplines. The research is concentrated in a handful of states and provinces. One article explicitly used Indigenous research methods, although Indigenous research participants were common in articles outside of the disciplines of history and law. Yet, those two disciplines dominate the current literature. We conclude that the community of scholars for whom relationships between parks and Indigenous Peoples are the central research question is smaller than those for whom it is one aspect of their research agenda.

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.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0110.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.344
Teacher spread0.270 · 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