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Record W1189284745 · doi:10.32920/22223260.v1

Living in Perfect Harmony: Harmonizing Sub-Artic Co-Management through Judicial Review

2023· article· en· W1189284745 on OpenAlexfundaboutno aff
Sari Graben

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
FundersQueen's University
KeywordsLegislationCorporate governanceHarmony (color)Political scienceTrilogyCitizen journalismPublic administrationRestructuringStatutory interpretationStatutory lawCLARITYLawLaw and economicsSociologyBusiness

Abstract

fetched live from OpenAlex

<p>To foster the participation of Aboriginal peoples in resource governance, the Government of Canada has recently restructured a number of administrative regimes, converting them into institutions of comanagement. Despite this restructuring, the degree to which Aboriginal peoples’ participation can influence the regulatory output of co-management boards remains uncertain in law. This article deconstructs one interpretive method that can impact participation in co-management regimes: harmonization. Drawing on a trilogy of cases, I argue that recent judicial efforts to harmonize the Mackenzie Valley Resource Management Act with its predecessor, the Canadian Environmental Assessment Act, can limit the regional interpretive differences that Aboriginal peoples’ participation in treaties and co-management is intended to foster. This outcome is problematic to the extent that it frustrates the participatory goals of the legislation and the substantive goals of contemporary treaties. In light of this problem, I advocate a cautious approach to statutory interpretation in which administrative boards tasked with ensuring Aboriginal participation in decision making can be expected to produce rules, decisions, and interpretations that differ from those produced under other regimes.</p> <p><br></p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.086
GPT teacher head0.367
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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