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Record W2592831766

ABORIGINAL PARTICIPATION IN ENVIRONMENTAL ASSESSMENTS FOR NATURAL RESOURCE DEVELOPMENT

2016· dissertation· en· W2592831766 on OpenAlexaboutno aff
Aniekan Udofia

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2016
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsNatural resourceEnvironmental planningNatural (archaeology)Environmental resource managementNatural resource managementResource (disambiguation)GeographyEnvironmental sciencePolitical scienceComputer scienceArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Aboriginal participation is a key component of environmental assessment (EA), and is recognized in various policy processes concerning natural resources development across Canada.Despite the recognition of the participation of Aboriginal peoples as foundational to effective EA, there are long-standing concerns about the limited influence of Aboriginal communities on decisions about developments on their traditional lands.The recent push for more effective and meaningful Aboriginal engagement in development decisions arises, in part, from increased industry and regulatory demand for the streamlining of EA to achieve a more efficient and timely EA process.Notwithstanding the increasing scholarly and policy literature on Aboriginal participation in EA, little research exists on viable solutions to advance meaningful Aboriginal participation in EA whilst maintaining a degree of process efficiency to support timely EA decisions about resource development.This thesis draws on the experiences from industry, government and Aboriginal communities involved in EA for mineral resource development in northwest Saskatchewan, Canada and case studies in international EA practices, to advance the effectiveness of Aboriginal participation in EA.Effectiveness is defined as participation that is both meaningful to those affected and efficient for those seeking development approvals.The research methodology includes the review of scholarly and policy research, several legal and EA case reviews and semi-structured interviews.First, this thesis examined the evolution of participation in EA in Canada, and the extent to which scholarly research has contributed to solutions for meaningful Aboriginal participation amidst increasing demands for a regulatory process that is more efficient and with shorter timelines for participation and decision-making.Second, attention is focused on the underlying practice-based challenges to meaningful and efficient Aboriginal participation in EA, explored through semi-structured interviews and a case study of EA in northwest Saskatchewan, and adopting a policy community model.Third, drawing on the international literature, case experience, and lessons from northwest Saskatchewan, reforms and enhancements to the current EA system are proposed to help ensure meaningful and efficient participation of Aboriginal peoples in EA processes.The thesis concludes with a discussion of the main findings, addresses specific recommendations to advance Aboriginal participation in EA for uranium development in northwest Saskatchewan, and identifies opportunities for future policy iii and scholarly research.Results of this research indicate that many of the challenges are multidimensional, and of considerable concern to both meaningful and efficient Aboriginal participation in EA.Understanding the nature of these underlying challenges requires increasing attention to the needs, expectations, roles and responsibilities of key actors engaged in the EA policy community, and exploring the much needed institutional and process reforms are critical to advancing meaningful Aboriginal participation in EA without compromising timely decisions for development proponents.

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)0.013
metaresearch head score (Gemma)0.015
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.740

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.008
Scholarly communication0.0060.002
Open science0.0020.009
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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