Those who get hurt aren't always being heard: Scientist-resident interactions over cummunity water
Bibliographic record
Abstract
Providing people with access to safe drinking water is one of the central problems of our times, especially in less developed countries. However, even in developed countries such as Canada, accessing safe drinking water may be difficult. In this case study, we articulate how scientific and technological expertise and discourse are played out against local knowledge and water needs to prevent the construction of a watermain extension that would provide local residents with the same water that others in the community already access. We draw on an extensive database constructed during a three-year ethnographic study of one community; the database includes the transcript of a public meeting, newspaper clippings, interviews, and communications between residents and town council. We show not only that scientists and residents differ in their assessment of water quality but also that there is a penchant for undercutting residents ’ attempts to make themselves heard. Who is an expert? 3 In our society, the stories of ordinary peoples ’ relationships to ordinary places remain
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".