New policy and regulatory reforms for Ontario Conservation Authorities
Bibliographic record
Abstract
First established in the 1940s, Ontario Conservation Authorities are internationally recognized as leading examples of integrated water resources management. In late 2021 and early 2022, the Ontario government published two reports focused on regulatory proposals and rules of conduct for conservation authorities (CAs). A primary aim of the provincial government’s pro-growth proposals was to increase the supply of affordable housing by speeding up new development review and approval processes. The following topics were identified as mandatory programs for CAs related to risks posed by natural hazards within their jurisdiction: flooding, erosion, dynamic beaches, hazardous sites as defined by a Provincial policy statement in 2020, and low water/drought. The overall intent of the Ontario government proposals is for CAs to focus on identifying natural hazards, assessing and managing associated risks, and improving public awareness of hazards. In this commentary, we summarize key changes for the CAs proposed by the Ontario government, and identify implications for the future, including CAs having less autonomy and discretion over core mandatory programs, increased emphasis on local funding, and municipalities having more say in CA programs and services for which they pay.
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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".