Mining for Thetford's Identity: Reclaiming the Mine Sites of a Former Asbestos Town
Bibliographic record
Abstract
Canada has a long history of production, use, and export of asbestos, starting in 1876 when the first Canadian asbestos mine opened.With the industrial era of asbestos nearing its end, postproduction asbestos towns will remain not only as urban entities but also -and perhaps more importantly -as communities.This thesis considers the question of a new architectural and landscape design strategy for Thetford Mines, a former asbestos town wavering between success and failure.At one time the driver of the city's economy and core of its identity, Thetford's asbestos mines are now an uncomfortable impediment to a holistic approach to urban development.This thesis addresses two main critical issues: 1) how to repurpose a former mine site?; and 2) how to reconcile a contested past with the town's presentday identity?The goal is to trigger both reconciliation with a problematic past as well as urban development for the present.This thesis would not have been possible without a number of important people.First, I owe my deepest gratitude to my advisor, Professor Janine Debanné, for her invaluable contribution to this thesis.Her presence throughout this year-long process was greatly beneficial and, more importantly, highly pleasant and appreciated.I also want to thank my thesis comrades, Michael Stock and Hannah Munroe, who have experienced along my side this rewarding, yet overwhelming, achievement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".