Froth Flotation and Kimberley, B.C. The Role of Technology in the Transition Between Mining Camp and Community
Bibliographic record
Abstract
Throughout the industrial age, the search for mineral resources has led to the creation of many\ntemporary resource extraction settlements, the vast majority of which never become long-term\ncommunities. For those who do, there is a critical point at which they transform from a largely singlesex work camp into a community with families and extended social and employment networks. While much research has been done on the growth and development of resource communities, far less has focused on this transitional period, and one aspect in particular has been especially neglected – the role of mining technology. This article examines the invention and application of one extremely influential\npiece of technology, the differential froth flotation method of ore separation, and its impact on the hardrock mining communities. Due to this technology’s extension of mine lifespan, long-term settlements could be developed around mine sites. In the case of twentieth century North American resource communities, this transition was also aided by research on crime and social trends that suggested advantages to living in smaller urban or suburban centers. This in turn influenced public policy that created a favorable atmosphere for the growth and survival of many small cities that had originated as resource extraction camps. This article examines these various contributing factors by using the example of a particular British Columbia community.
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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 | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".