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Record W7161946831 · doi:10.82308/45236

Unearthing the discursive politics of mining on Indigenous lands: knowledge, health, contestation, and power in contemporary Canadian regulatory infrastructures

2022· dissertation· en· W7161946831 on OpenAlexaboutno aff
Ella Myette

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTechnocracyFraming (construction)PoliticsGovernment (linguistics)Context (archaeology)Colonialism

Abstract

fetched live from OpenAlex

Mining projects have the potential to significantly affect Indigenous Peoples and their health in myriad ways, both in the short term and across generations. During the approval stage for new extractive sites, a projects’ anticipated impacts on Indigenous Peoples’ health and wellbeing are evaluated using a process called environmental assessment (EA). However, EA is a technocratic process that relies on and advances a very specific and narrow understanding of health— one based in Western colonial cultural understandings and assumptions that can be inappropriate and even harmful for Indigenous communities.This thesis sought to answer two key research questions: how can extractive projects affect Indigenous Peoples’ health, and how is Indigenous Peoples’ health represented in environmental assessment? My methods to answer these questions included a scoping review of the literature on extraction and Indigenous Peoples’ health, as well as a qualitative document analysis of the final environmental assessment reports for 28 mining projects in Canada. I then interpreted the results of these analyses using critical framing techniques borrowed from infrastructure studies to unpack the broader political implications of EA and the kind of knowledge it contains and perpetuates. In the scoping review, I identified a set of mechanisms with the capacity to be activated in an extraction context and produce health outcomes, including: engagement in assessment and consultation processes; interaction with government and industry officials; the presence and nature of new work and training opportunities; changes to the economy and an influx of new money; changing social structures and new inequalities; environmental degradation and dispossession; new and longstanding changes to the economy; and lasting effects on land. The variation in these mechanisms across space and time confirms that communities can be affected by resource projects via a wide range of pathways, which proves that a holistic perspective is necessary to adequately measure and understand effects on Indigenous Peoples’ health. However, the analysis of the EA reports showed that these varied pathways to health were often not included in the assessment process. And, when indicators related to Indigenous health were included, these methods of assessment were at odds with community health ontologies due to their narrow focus and their inability to consider the complexity and essentiality of human-non-human relations. The primary conclusion of this thesis is that EA is largely ineffective at fully or accurately assessing effects of extractive projects on Indigenous Peoples’ health. Beyond identifying a diverse set of fundamental issues with EA’s measurements, I also argue that the process itself leads to a furthering of colonial logics in the public sphere, which is harmful to communities and contributes to asymmetrical power dynamics between Indigenous Peoples and the Crown

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)0.017
metaresearch head score (Gemma)0.018
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 score0.155
Threshold uncertainty score0.980

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.010
Science and technology studies0.0270.049
Scholarly communication0.0160.005
Open science0.0030.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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
Published2022
Admission routes1
Has abstractyes

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