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Record W2905272262 · doi:10.1111/capa.12301

Understanding consultation and engagement of Indigenous Peoples in resource development: A policy framing approach

2018· article· en· W2905272262 on OpenAlexfundaboutno aff
Brendan Boyd, Sophie Lorefice

Bibliographic record

VenueCanadian Public Administration · 2018
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousFraming (construction)Political sciencePublic relationsPolicy developmentResource (disambiguation)Public administrationGovernment (linguistics)BusinessGeography

Abstract

fetched live from OpenAlex

Abstract Canada’s legal system has repeatedly ruled that the Crown has a duty to consult with Indigenous Peoples when approving and shaping resource development projects that are located on their land or could infringe on their rights. Yet, there are still incidences where Indigenous communities and organizations find formal consultation processes, and the approach to engagement taken by industry and government, to be lacking. We use insights from the policy studies literature to argue that generating more evidence and analysis about the benefits and impacts of development is unlikely to improve consultation and engagement processes or resolve resource development disputes. We suggest that a policy framing approach, which examines how the different actors frame or define controversial and intractable policy problems, can provide insight into why disputes occur. We examine publicly available documents and statements about consultation and engagement produced by Indigenous groups, Canadian governments, and industry to identify and compare how these groups are likely to frame resource development and consultation activities.

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)0.029
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.270
Threshold uncertainty score0.849

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0310.052
Scholarly communication0.0170.010
Open science0.0030.011
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.052
GPT teacher head0.235
Teacher spread0.183 · 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 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

Citations30
Published2018
Admission routes2
Has abstractyes

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