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Record W7139191868

Private Motives, Public Benefits: The role of Conservation Easements in Canadian Biodiversity Conservation

2025· dissertation· W7139191868 on OpenAlexaboutno aff
Forrest Hisey

Bibliographic record

VenueTSpace (University of Toronto) · 2025
Typedissertation
Language
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEasementLivelihoodPrivate propertyBiodiversityLegislationBiodiversity conservationWildlife conservationPublic land
DOInot available

Abstract

fetched live from OpenAlex

Amid increasing urban sprawl, global temperatures, a growing population, and shrinking habitat, there are calls to expand and transform biodiversity conservation strategies. In response, I present four cases focused on the role of Conservation Easements (CEs) in Canadian private land conservation (PLC), examining how they support, impact, and transform individuals, institutions, and broader socio-ecological contexts. I use qualitative studies to a) examine their theoretical relationship with private property logics, relational connections between individuals and communities, and their place within settler-colonial logics of racism, access, and control; b) how their subnational legislation was developed and influenced across the provinces and territories of Canada; c) what motivates landowners and land trusts in Alberta to rely primarily on CEs for PLC; and why a unique grazing co-operative in southwestern Alberta has used CEs as both a means to support sustainable livelihoods and biodiversity, while rapidly expanding their landholdings via PLC payments. I employ political ecology to analyze policy documents, grey literature, existing literature, survey data, and semi-structured interviews. Considering questions of access, private rights, and public responsibilities, I argue that CEs unevenly fit within existing private-public property binaries but provide a unique PLC tool that can support conservation objectives by cultivating a constellation of diverse relationships within and beyond fence lines. This research is foundational as it presents the first in-depth qualitative analysis of the multifaceted nature of CEs in a purely Canadian context.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.647

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.014
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.211
Teacher spread0.197 · 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 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
Published2025
Admission routes1
Has abstractyes

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