Pre-released chapter from A Breath of Fresh Air: Market Solutions for Improving Canada’s Environment
Bibliographic record
Abstract
Air quality in Canada has substantially improved since the 1970s and at least some of this improvement can be attributed to the detailed, extensive system of legislation currently in place to control air pollution. New initiatives in air pollution legislation, including the proposed Clean Air Act, should take into account the fact that air quality is already regulated and that pollution has already been substantially reduced. This chapter describes the evolution of Canadian air quality since the early 1970s and discusses the scientific question of whether current air pollution levels are a threat to human health. It then de-scribes the existing structure of Canadian air-pollution policy, including the new focus on ultrafine particles and the introduction of Air Quality Indexes. I conclude by outlining some general principles that should guide policy-mak-ers for developing future air-pollution legislation. I argue that policy-makers should begin by focusing on giving people access to objective, accurate, and up-to-date information on pollution levels and trends, as well as helping them to understand the existing structure of air-pollution regulations that affect their regions. I also argue for flexible, locally-tailored initiatives that give people more direct say in the level of environmental quality they enjoy, and for more exploration of the use of emission-pricing instruments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.014 |
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".