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Record W3022547310 · doi:10.1177/0308518x20924027

Counter-institutionalizing First Nation–Crown relations in British Columbia

2020· article· en· W3022547310 on OpenAlexaffabout
Anthony W. Persaud, Terre Satterfield, Eliana Macdonald

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2020
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLeverage (statistics)IndigenousDutyCorporate governanceDilemmaTreatyPolitical scienceAuditSelf-governanceFirst nationPublic administrationBusinessLawFinanceAccountingComputer science

Abstract

fetched live from OpenAlex

In Canada, the advance of industrial resource extraction has been moderated by a series of key legal decisions that have found that development activities within the traditional territories of Indigenous Nations may infringe on Aboriginal and treaty rights, requiring a duty to consult and potentially accommodate those affected. In British Columbia this duty is primarily satisfied through the Crown referrals process, whereby affected First Nation groups are notified by the Crown regarding potential rights-affecting decisions and are given an opportunity to formulate a response. This form of institutionalized engagement presents an ongoing challenge for First Nation groups who struggle to manage the influx of Crown referrals, as well as a dilemma for those who question its fairness and inherent colonial structure. For others, it is seen as an opportunity to leverage the duty to consult and accommodate in order to strengthen territorial self-governance. In this paper we introduce the idea of counter-institutionalizing and explore the conditions under which the Crown referrals process is being redrawn to better address, and not, the ability of First Nation groups to improve territorial self-governance and the trade-offs involved.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.158
Teacher spread0.145 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2020
Admission routes2
Has abstractyes

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