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Record W1564116160 · doi:10.1111/ssqu.12115

A Historical Institutionalist Understanding of Participatory Governance and Aboriginal Peoples: The Case of Policy Change in Ontario's Mining Sector*

2014· article· en· W1564116160 on OpenAlexaffabout
J. Andrew Grant, Dimitrios Panagos, Michael C. Hughes, Matthew I. Mitchell

Bibliographic record

VenueSocial Science Quarterly · 2014
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsSaint Paul UniversityMemorial University of NewfoundlandQueen's University
Fundersnot available
KeywordsCorporate governanceCitizen journalismLegislationNatural resourcePoliticsPublic administrationParticipatory action researchConstitutionState (computer science)Resource (disambiguation)Political scienceSociologyEconomic growthEconomicsLawManagement

Abstract

fetched live from OpenAlex

Objective Natural resource policy has been a constant source of conflict between “Aboriginal” and “non‐Aboriginal” stakeholders in Canada. We employ a historical institutionalist analysis to examine the extent to which changes to the Canadian Constitution in 1982 and Ontario's Mining Act in 2009 enabled Aboriginal communities to become equal partners in participatory governance arrangements in mineral resource sectors. Methods We analyze primary sources consisting of federal and provincial legislation and in‐person interviews conducted across Ontario in 2010. Results The existing Canadian mining policy paradigm, while under significant pressure, has not yet been displaced by a new policy paradigm that would better accommodate the interests of Aboriginal stakeholders. Consequently, Aboriginal peoples’ mineral resource claims are likely to remain unresolved. Conclusion We suggest how a policy paradigm that both improves Aboriginal‐state relations and reduces uncertainty in the mining sector offers a promising political foundation for participatory governance and cooperative engagement between stakeholders.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.944

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 head0.050
GPT teacher head0.283
Teacher spread0.232 · 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 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

Citations8
Published2014
Admission routes2
Has abstractyes

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