Mining scars of single industry communities: an architectural response to the ecological impact of the mining industry in the Lakeshore Basin, Kirkland Lake, Ontario.
Bibliographic record
Abstract
This research aims to create a better understanding of the ecological economies and cultural identity within industrial communities and create a strategy for the second life of single industry cities and towns. Communities which are dependent on a single industry for employment become established in parallel with industrial economies, industry in turn becomes integrated in their identity, landscape and urban fabric. With a high number of these towns and cities reliant on mining in particular, they become incredibly susceptible to world price fluctuations. 1 The mine within the community is a double-edged sword, in that through settlement it provides jobs and economic benefits, but in its reliance on finite minerals it creates an unsustainable resource for the community. Having been born and raised in Kirkland Lake, Ontario which is a single industry mining town, I have a good understanding that this reliance guides many communities into boom bust cycles, which ultimately leads to population decline, decreased local services, and reduced property value. 2 This understanding has led me to choose Kirkland Lake as the location for my thesis, my connection to the community will be an asset within this body of research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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".