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Record W4235589845 · doi:10.32920/ryerson.14656179.v1

Filling the integrity gaps in protection of species at risk through litigation and stewardship : Policy evaluation with a focus on Ontario

2021· preprint· en· W4235589845 on OpenAlexafffundabout
Khadijah Moinuddin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
FundersU.S. Fish and Wildlife ServiceMinistry of Education, IndiaOntario Power AuthoritySociety of Canadian OrnithologistsEnvironment and Climate Change CanadaMinistry of EnvironmentBird Studies CanadaNational Marine Fisheries ServiceMinistry of Natural ResourcesWorld Wildlife Fund
KeywordsStewardship (theology)Government (linguistics)BusinessPolitical scienceAction (physics)Public administrationRisk managementPublic relationsEnvironmental planningLawGeographyPoliticsFinance

Abstract

fetched live from OpenAlex

There are many integrity gaps in the federal and provincial systems designed to protect endangered and threatened species in Canada. NGOs (Non-governmental organizations) and other stakeholders can get involved in the process of protecting species at risk, by participating in collaborative efforts through volunteerism and stewardship efforts. NGOs can also bring issues of species-at-risk protection to court through litigation. This thesis began by exploring the integrity gaps in the federal and provincial processes for species at risk protection. The thesis then examined different initiatives undertaken by NGOs to combat this issue, the first being litigation and how it can be used as a strategy to help protect species at risk, and hold government accountable. The thesis also explored the rationale behind NGO actions, as well as the possible outcomes from these court cases. Next, the thesis discussed volunteer efforts undertaken by NGOs and other stakeholders. The research was supplemented with valuable qualitative interview data from scientists, members of NGOs, members of government, and a lawyer. The thesis concluded with recommendations for further action and policy measures that can be taken to protect species at risk

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.259
Teacher spread0.209 · 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.

Study designObservational
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

Citations0
Published2021
Admission routes3
Has abstractyes

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