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Record W4400088804 · doi:10.1080/07011784.2024.2370199

New policy and regulatory reforms for Ontario Conservation Authorities

2024· article· en· W4400088804 on OpenAlexaffvenueabout
Bruce Mitchell, Dan Shrubsole, Nigel Watson

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsWestern UniversityUniversity of Waterloo
Fundersnot available
KeywordsRegulatory reformPublic administrationBusinessEnvironmental planningPolitical scienceEnvironmental scienceLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.202
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes3
Has abstractyes

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