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Navigating Indigenous heritage protection and local land use planning on the island of Xwe'etay, British Columbia

2025· article· en· W4414000106 on OpenAlexafffundvenueabout
Madeleine MacLean, Sean Markey, Dana Lepofsky

Bibliographic record

VenueCanadian Planning and Policy / Aménagement et politique au Canada · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaSimon Fraser University
KeywordsIndigenousGeographyEnvironmental planningArchaeologyCultural heritageLand useEnvironmental resource managementEnvironmental protectionCivil engineeringEcologyEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

In many contexts around the world, colonial-settler heritage policies and local planning processes contribute to the ongoing loss of Indigenous archaeological heritage (IAH). Many intertwined factors contribute to this loss, including colonial systems that do not recognize the inherent rights of Indigenous people to manage their heritage on their own terms. Here, we explore the challenges that heritage policies and local planning systems present to robust IAH conservation. We focus on the island of Xwe’etay, in the Salish Sea of British Columbia and the governing bodies that control or have an interest in its heritage. Our interviews with local planners, First Nations, and others inform three central issues foundational to IAH planning: 1) jurisdiction and planning scope; 2) regulation and enforcement; and 3) capacity and resource limitations. We find that top-down, colonial regulations that guide heritage management practices are failing to adequately protect Indigenous archaeological heritage, while also instilling uncertainty and fear among settler landowners. Engaging First Nation and settler community members in IAH conservation, active relationship-building, knowledge mobilization, and enhanced local jurisdiction may present pathways to better protect IAH.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.328
Teacher spread0.302 · 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 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

Citations0
Published2025
Admission routes4
Has abstractyes

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