Cross-sectoral input for the potential role of science in Canada’s environmental assessment
Bibliographic record
Abstract
Since being elected in 2015, Canada’s federal Liberal government has taken steps to overhaul major environment-related laws and policies, including federal environmental assessment (EA) and regulatory processes. During 2016–2017, a government-appointed panel toured Canada and received >1000 suggestions from diverse sectors of society regarding EA reform. Yet, different sectors of society may have different views concerning scientific components of EA. We analyzed written submissions during public consultation (categorized into five sectors) regarding five key scientific components of EA: (1) openly sharing information, (2) evaluating cumulative effects, (3) scientific rigour, (4) transparency in decision-making, and (5) independence between regulators and proponents. On the whole, submissions from Indigenous groups, non-governmental organizations, and individuals/academics supported strengthening all five components. In contrast, most contributions from industry/industry associations, and, to a lesser extent, government bodies or agencies, suggested that there was no need for increased scientific rigour or increased independence. These findings indicate that there is cross-sectoral support for strengthening some scientific aspects of EA. However, the degree to which the Government of Canada strengthens the scientific rigour and independence of EA will indicate whether environmental decision-making in Canada is aligned with preferences from industry or the rest of Canada.
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 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 | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".