Ghost Towns and Zombie Mines: The Historical Dimensions of Mine Abandonment, Reclamation, and Redevelopment in the Canadian North
Bibliographic record
Abstract
In the past two decades a new approach to mining history has emerged to ask, in effect, what happens after the gold rush. Authors such as Richard V. Francaviglia, Ben Marsh, William Wyckoff, and more recently David Robertson have all extended their narratives beyond the demise of mining towns to question what they consider to be the “mining imaginary,” the idea that the historical end-point for mining activity is inevitably community collapse and ecological destruction. They provide valuable case studies where communities have survived past the end of mining, diversifying their economies through industrial activity or the development of tourism. Historical memory often provides a sense of continuity for these communities, as mining heritage landscapes and museums become touchstones of tourist activity, and ecological restoration activities reveal a deep sense of attachment to the mining landscape. For this loosely defined community resilience school of mining history, mining is not an ephemeral economic activity but offers communities a long-term sense of deep intimacy with their history of labour within the local landscape.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".