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Record W4407155305 · doi:10.1139/facets-2024-0016

Provincial diffusion, national acceptance: the transfer of conservation easement policy in Canada

2025· article· en· W4407155305 on OpenAlexaffvenueabout
Forrest Hisey

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEasementBusinessDiffusionGeographyEnvironmental planningPolitical scienceLaw

Abstract

fetched live from OpenAlex

Conservation easements (CEs) are a private land conservation (PLC) tool, with landowners voluntarily selling property rights to an outside entity (governmental or nongovernmental). Pioneered in the USA, CEs were operationalized in the late 1980s, and by 2001, legislation had swept across Canada. I asked how did subnational Canadian CE policy develop? I analyzed Hansard records and interviewed government officials, finding coercion from the Federal government and environmental nongovernmental organizations (eNGOs), with transfer being ideologically, geographically, and temporally uneven. CE legislation reveals a fundamental shift in how subnational governments were trying to enhance biodiversity conservation, specifically by legitimizing PLC and non-state partners. Interestingly, this study both confirms, and pushes back against, previous Canadian policy transfer studies. I found a lack of formal subnational policy networks and an increased role of subnational policy innovators unlike previous studies, while the substantial U.S. influence align with older policy cases. ENGOs were the most active proponents to push for CE legislation, not policymakers or foreign states. Ultimately, Canadian federalism creates unique subnational policy arenas that require further study to understand the movement of conservation policy, especially with the crises of biodiversity and climate.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.944

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0130.004
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.230
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2025
Admission routes3
Has abstractyes

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