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Record W7062932084

What Happens After The Mine?: A critique of approaches to the design, remediation, and perpetual care of post-extraction landscapes in Canada

2022· dissertation· en· W7062932084 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPerpetuityHarmGovernment (linguistics)Land reclamationNatural resourceContaminated landEnvironmental remediationSustainability
DOInot available

Abstract

fetched live from OpenAlex

Mining produces enormous amounts of waste, often toxic, that requires containment, record keeping, and monitoring in perpetuity to ensure it does not harm the surrounding ecosystem. Post-extraction landscapes in Canada receive inadequate remediation and care. Current mine closure regulations and remediation practices focus on short term, technical toxin mitigation strategies on site and ignore the diverse impacts of mining on surrounding communities and non-human species. The push for a global transition to renewable energy paradoxically requires an unprecedented increase in mining for minerals used in wind turbines, solar panels, and batteries. The Government of Canada is taking the opportunity to capitalize on this global demand for its natural resources by exploring, developing, and opening new mines. In doing so, it is consequently creating new permanent waste sites each year, adding to thousands of existing abandoned mines not yet remediated. \n \nLeaning on post-extractivist literature, this thesis analyzes a range of approaches to reclamation and remediation of post-mining landscapes through three themes: (1) perpetuity – acknowledging the deep time extraction processes rely upon and the endurance of toxins, critiquing the short-term thinking of the mining industry, and advocating for long-term planning of care practices; (2) communities – recognizing that negative impacts of extraction on humans and nature are interconnected, reflecting on the colonial history of mining, and arguing that remediation must include social healing; (3) ecosystems – exploring the limits of property and profit driven practices, and recognizing the importance of multi-species, watershed scale remediation and care. \n \nPart 1 examines government regulations and the mining industry’s mine closure and reclamation practices. Part 2 considers the role of architects and landscape architects in the design of post-extraction sites, by reviewing projects and identifying stakeholder motivations and values. Part 3 discusses post-extractivist frameworks which advocate for a more holistic remediation and explores theory on concept of perpetual care, through the themes of perpetuity, communities, and ecosystems. Part 4 studies two community initiatives, which demonstrate aspects of perpetual care. These case studies gather and synthesize a range of existing documents, including technical assessment and remediation reports, community member testimonies, and socio-ecological data. Writing and drawings contrast the mining industry’s perspective and values with community perpetual care. Finally, the thesis reflects on the mining industry, government, designer, and community’s approaches to post-extraction sites, and considers the potential role of designers in mine reclamation. It argues that the themes of perpetuity, communities, and ecosystems are overlooked and need to be addressed for successful design, remediation, and perpetual care of post-extraction landscapes.

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)0.010
metaresearch head score (Gemma)0.014
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: Empirical · Consensus signal: none
Teacher disagreement score0.314
Threshold uncertainty score0.795

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0530.119
Scholarly communication0.0270.011
Open science0.0120.009
Research integrity0.0120.022
Insufficient payload (model declined to judge)0.0040.000

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 head0.193
Teacher spread0.179 · 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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