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Record W4408147911 · doi:10.1016/j.rama.2025.01.009

The Social Fit of Conservation Policy on Working Landscapes

2025· article· en· W4408147911 on OpenAlexafffundabout
Jeremy Pittman, Raphael Anammasiya Ayambire, Kwaku Owusu Twum

Bibliographic record

VenueRangeland Ecology & Management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of ManitobaUniversity of Waterloo
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsGeographyEnvironmental resource managementBusinessEnvironmental planningEnvironmental science

Abstract

fetched live from OpenAlex

The working landscapes approach is valuable for extending conservation beyond the boundaries of strict protected areas. Conservation on working landscapes relies heavily on social acceptance and the alignment of conservation programs with local livelihoods. This paper examines farmers and ranchers’ preferences for different policy instruments and incentives that form programs for endangered species conservation in Canada's temperate grassland ecosystem—one of the most imperiled ecosystems on earth. Generally, farmers and ranchers are more concerned about the restrictions that programs impose than they are about the amount of funding the programs provide. Although, trust in the program delivery agent is also a key consideration. Overall, farmers and ranchers prefer instruments that maintain their property rights and provide continuous financial incentives. Additionally, they prefer shorter-term contracts to longer-term contracts or agreements in perpetuity. Many of their preferences extend beyond status quo conservation in Canada, which relies heavily on restrictions, non-continuous financial incentives (i.e., one-time payments), and long-term agreements. We need to augment the existing suite of programs to include flexible and adaptive options to maintain, improve and protect grasslands and the species that depend on them.

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 score0.104
Threshold uncertainty score0.322

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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
Published2025
Admission routes3
Has abstractyes

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