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Record W2891560180 · doi:10.18584/iipj.2018.9.3.3

Reflection, Acknowledgement, and Justice: A Framework for Indigenous-Protected Area Reconciliation

2018· article· en· W2891560180 on OpenAlexaffvenueabout
Chance Finegan

Bibliographic record

VenueInternational Indigenous Policy Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsYork University
Fundersnot available
KeywordsIndigenousCommitAcknowledgementColonialismEconomic JusticeHarmPolitical scienceIndigenous rightsSociologyEnvironmental ethicsLawEcology

Abstract

fetched live from OpenAlex

Protected areas have been both tools and beneficiaries of settler colonialism in places such as Canada, Australia, and the United States, to the detriment of Indigenous nations. While some agencies, such as Parks Canada, increasingly partner with Indigenous nations through co-management agreements or on Indigenous knowledge use in protected area management, I believe such efforts fall short of reconciliation. For protected areas to reconcile with Indigenous Peoples, they must not incorporate Indigeneity into existing settler-colonial structures. Instead, agencies must commit to an Indigenous-centered project of truth telling, acknowledging harm, and providing for justice. I begin this article by outlining what is meant by reconciliation. I then argue for protected area-Indigenous reconciliation. I conclude with a framework for Indigenous–settler reconciliation within protected areas.

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)0.061
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0250.157
Scholarly communication0.0260.029
Open science0.0080.026
Research integrity0.0160.015
Insufficient payload (model declined to judge)0.0050.001

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 head0.050
GPT teacher head0.415
Teacher spread0.365 · 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 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

Citations37
Published2018
Admission routes3
Has abstractyes

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