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Record W799407692

ENVIRONMENTAL IMPACT ASSESSMENTS AND IMPACT BENEFITS AGREEMENTS: THE PARTICIPATION OF ABORIGINAL WOMEN AT VOISEY’S BAY MINE

2013· dissertation· en· W799407692 on OpenAlexfundaboutno aff
David Cox

Bibliographic record

VenueMacSphere (McMaster University) · 2013
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
FundersMcMaster University
KeywordsBayEnvironmental impact assessmentEnvironmental planningEnvironmental protectionGeographyEngineeringCivil engineeringPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Resource extraction projects in the North are governed by negotiated agreements developed between industry, the state and Aboriginal governments and institutions. This thesis examines the role played by women in the Environmental Impact Assessment (EIA) and Impact Benefit Agreement (IBA) processes leading up to Voisey’s Bay mine in northern Labrador and whether women’s involvement in resource governance improves the participation and retention of women in non-traditional jobs at the mine. Using a qualitative methodology of semi-structured interviews and thematic analysis, this thesis found that the participation of Aboriginal women was unable to significantly improve the work experiences of women at the mine. The concerns of Aboriginal women were identified by analyzing submissions made to the EIA panel by women’s groups. These concerns were then compared with the perceptions of work by women who worked in either construction or the operations phase of the mine. The confidentiality of IBA negotiations and documents are offered as one reason that Aboriginal women did not have the concerns they raised during the EIA process mitigated. The unfinished IBA was referred to by VBNC, and accepted by the panel, as a way to mitigate women’s concerns despite confidentiality preventing the contents of the IBA from ever being known. While women received prioritization in the IBA, Aboriginal women demanded quotas and targets for the training and hiring of women for the construction and operations phase. The thesis ends with a discussion of ways to alleviate the conflict between IBA and EIA processes.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

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)0.0950.000

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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.

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

Citations0
Published2013
Admission routes2
Has abstractyes

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