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Record W4207033824 · doi:10.33002/jelp01.03.03

Biodiversity and Conservation: Cross-Border Legal and Regulatory Perspectives

2021· article· en· W4207033824 on OpenAlexfundvenueaboutno aff
Alexandra R. Harrington, Konstantia Koutouki

Bibliographic record

VenueJournal of Environmental Law & Policy · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
FundersEuropean CommissionGovernment of CanadaHarvard Business School
KeywordsThreatened speciesPolitical scienceCorporate governanceBiodiversityState (computer science)ConstitutionPublic administrationEnvironmental resource managementGeographyEnvironmental planningLawBusinessEcologyEconomics

Abstract

fetched live from OpenAlex

This article provides an overview of the legal and policy frameworks for the protection of threatened and vulnerable wildlife on private lands in Canada and the United States, the approaches adopted in different jurisdictions and the response of key constituencies, and formulates recommendations based on these experiences. Canada and the United States serve as an important source of comparison in terms of biodiversity protection mechanisms for several reasons, ranging from geography and legal systems protections to shared economic concerns and development. Additionally, the shared fundamental dichotomy between governance at the national/federal level and the provincial/state level is a key area of comparison since there are many overlaps in these elements of governance across systems. At the same time, these relationships are governed subject to different forms of legal imperatives given the nature of articulated national and subnational powers and roles in Canadian law and the Constitution of the United States. Since both systems give primacy of place in law and regulation related to biodiversity and associated resources to the national/federal level, any comparisons must start at this level.

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)0.000
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 score0.181
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.254
Teacher spread0.225 · 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

Citations1
Published2021
Admission routes3
Has abstractyes

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