The Dog that Doesn't Bark: Federal Regulation of Industrial Air Pollution in Canada.
Bibliographic record
Abstract
In a well-known Sherlock Holmes story, Holmes solved a murder mystery by pointing to the [“curious incident of the dog in the night-time”](https://brieflywriting.com/2012/07/25/the-dog-that-didnt-bark-what-we-can-learn-from-sir-arthur-conan-doyle-about-using-the-absence-of-expected-facts/). “The dog did nothing in the night-time”, countered the Scotland Yard detective on the case. “That was the curious incident” replied Holmes. [Health Canada](https://www.canada.ca/en/health-canada/services/publications/healthy-living/2021-health-effects-indoor-air-pollution.html) estimates that air pollution accounts for 15,300 premature deaths annually in Canada. All the key air pollutants are found on Canada’s [list of toxic substances](https://www.canada.ca/en/environment-climate-change/services/canadian-environmental-protection-act-registry/substances-list/toxic.html), giving Environment and Climate Change Canada (ECCC) full authority to regulate emissions. And yet there are only a handful of federal regulations addressing air pollution from industrial/stationary sources. This case study addresses the question – why doesn’t the dog bark?
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| 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; 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".