Growing Resolve: A Review of An Introduction to Environmental Law and Policy in Canada by Paul Muldoon, Alastair Lucas, Robert Gibson, and Peter Pickfield
Bibliographic record
Abstract
In 2005, commenting on a government review of the main federal toxic substances control legislation, Jason Unger aptly described the general public’s usual role in Canadian environmental law: “[They are] left to wander a maze of legislative and non-legislative instruments, each with varying amounts of transparency, to determine whether standards for a particular substance exists, what the standards are, whether they are being met and whether they can take legal action to enforce them.” Explaining our system of pollution control and resource management law to the general public — where it came from, why it was chosen, in what way it (even remotely) seems rational, how it works, what flaws it has, how to use it, and how one might improve it — is a daunting task indeed. Nevertheless, authors Paul Muldoon, Alastair Lucas, Robert Gibson, and Peter Pickfield set out in An Introduction to Environmental Law and Policy in Canada to provide a primer on these issues for interested students and members of the public.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.040 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".