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Record W4249346050 · doi:10.32920/ryerson.14652345.v1

Bridging science and law across jurisdictions in Canadian species at risk policy : four case studies

2021· preprint· en· W4249346050 on OpenAlexaffabout
Maria-Lena Di Giuseppe

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLegislationStewardship (theology)Political scienceGeographyEnvironmental planningBusinessEnvironmental resource managementLawEconomics

Abstract

fetched live from OpenAlex

1.0 Introduction -- 2.0 Methods -- 3.0 Federal Legal Measures for Species at Risk in Canada -- 4.0 Provincial Legal Measures for Species at Risk in Ontario -- 5.0 Ontario Species at Risk: Two Case Studies -- 6.0 Provincial Legal Measures for Species at Risk in British Columbia -- 7.0 British Columbia Species at Risk: Two Case Studies -- 8.0 Policy Recommendations -- 9.0 Conclusions and Applications for Future Research. The purpose of this thesis is to evaluate the effectiveness of current legal measures for protecting species at risk in Canada through an interpretive qualitative method. Four species case studies were analyzed: The Eastern Loggerhead Shrike, Jefferson Salamander, Northern Spotted Owl, and Vancouver Island Marmot. Policy recommendations for reforms arising from the research are: i) inter-jurisdictional cooperation is imperative for protecting species at risk; ii) dedicated species at risk legislation is crucial, and it is recommended that such legislation exist at both federal and provincial levels; iii) flexibility instruments and exemptions to existing law should be scientifically informed and used cautiously; iv) private landowners are significant stakeholders and stewardship efforts are important; v) scientific information and the definition of critical habitat for species at risk are crucial. The thesis concludes that a science-based precautionary approach to species protection is fundamental to address the plight of species at risk in 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.005
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 head0.034
GPT teacher head0.296
Teacher spread0.262 · 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 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 routes2
Has abstractyes

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