Dimensions of Community Change: How the Community of Sudbury Responded to Industrial Exposures and Cleaned up its Environment
Bibliographic record
Abstract
A city in northern Ontario, which has suffered more than a century of pollution from mining, went from being internationally notorious for its pollution to winning awards for its environmental restoration. The inquiry was into the levers of change that led from an awareness of environmental destruction to taking action. Semi-structured interviews were conducted with 60 people from the community, politicians, industry, miners, and academics. The theory-based analysis led to a community-change model that has helped identify the multiple layers of change required for the re-greening of the environment. With reference to the collective impact literature, this city-level case study found that the city has embraced change based upon agreement on an emerging vision, taking advantage of a confluence of timing and events, adopting evidence-based knowledge, building a sense of pride and place, and having a diffuse yet linked leadership. The Sudbury story is helpful for other industrial communities looking to achieve change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.012 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".