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Record W2788082235 · doi:10.22215/etd/2015-10858

Governmentality and Mining: Analyzing the Environmental Impact Assessment for the Mary River Mine, Nunavut, Canada

2015· dissertation· en· W2788082235 on OpenAlexaboutno aff
Andrew Williams

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsObjectivity (philosophy)GovernmentalityEnvironmental planningEnvironmental impact assessmentNarrativeNatural resourceGeographyEnvironmental resource managementNatural (archaeology)Political scienceArchaeologyEnvironmental scienceLaw

Abstract

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The purpose of this research is to examine the thoroughness, objectivity, and inclusivity of the Environmental Impact Assessment (EIA) for the Mary River Iron Mine as a means to evaluate broader tensions expressed by the people of Nunavut that impact assessments are not addressing the concerns of northern communities.The EIA process in Nunavut is often conceptualized as a rigorous and unbiased tool that provides decision-makers with the information necessary to determine the likely impacts of a natural resources development project.This research reveals that the Mary River Project's potential to negatively impact caribou and Inuit harvesting of caribou was not thoroughly assessed, nor was it meaningfully informed by those concerned about the mine (e.g.Inuit organizations and residents of potentially impacted communities).As currently practiced, EIA privileges the perspectives of the mining industry and reinforces narratives that mining is the key to Nunavut's socio-economic development.Settlement Area 1 in order to assess their likely socio-economic and environmental impacts (NIRB, 2014).The NIRB's role is to facilitate open dialogue between the various actors involved or impacted by a project proposal, and to assess the Environmental Impact Statement (EIS) submitted by the project proponent.The review process involves a number of stages such as issues scoping, preparation and review of the EIS, and community consultations (NIRB, 2014B).In theory this process allows actors to express their concerns and opinions, and facilitates the collection and sharing of scientific data, as well as Inuit knowledge, known as Inuit Qaujimajatuqangit (IQ) 2 , as evidence to predict the likely socio-economic and environmental impacts of the project being assessed.Upon completion of a review, the NIRB reports its recommendations on whether or not a project should be approved, and under what conditions, to the Federal Minister of Aboriginal Affairs and Northern Development Canada (AANDC) who has the ultimate say regarding approval (NIRB, 2009A).Despite having a systematic procedure for conducting Environmental Impact Assessments (EIA), the NIRB's role is made more difficult due to the complex and often contested nature of mining and development plans in Nunavut.When plans for large-scale development projects are proposed, a number of conflicting perspectives often emerge.Narratives about sustainable development, Inuit self-determination, 1 The Nunavut Settlement Area is the geographical area that would become the Territory of Nunavut as defined in the NLCA.For a more detailed description of the geographical extent of the area, as well as its legal significance, see Article 3 in the NLCA (1993). 2 Also referred to as Traditional Knowledge, Traditional Ecological Knowledge, Aboriginal Knowledge, and Inuit Qaujimajatuqangit within Nunavut.I will use the term Indigenous Knowledge (IK) when speaking in a broad sense, and Inuit Qaujimajatuqangit (IQ) when referring to Inuit knowledge specifically.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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
GenreOther

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

Citations1
Published2015
Admission routes1
Has abstractyes

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