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Record W3187301909 · doi:10.1139/facets-2020-0071

Incentivizing stewardship in a biodiversity hot spot: land managers in the grasslands

2021· article· en· W3187301909 on OpenAlexaffvenueabout
Raphael Anammasiya Ayambire, Jeremy Pittman, Andrea Olive

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsStewardship (theology)IncentiveBusinessGovernment (linguistics)BiodiversityEnvironmental resource managementPortfolioEnvironmental planningAgricultureEcosystem servicesLand managementGeographyEcosystemPolitical scienceFinanceEcologyEconomics

Abstract

fetched live from OpenAlex

Federal and provincial governments of Canada recently signed onto a Pan-Canadian Approach to Transforming Species at Risk Conservation. The approach is based on collaboration among jurisdictions and stakeholders to enhance multiple species and ecosystem-based conservation in selected biodiversity hot spots. In this review paper, we focus on one of the biodiversity hot spots—the South of the Divide area in the province of Saskatchewan—to propose appropriate mechanisms to incentivize stewardship on agricultural Crown lands. Through a focused review and synthesis of empirical studies, we propose a range of policy instruments and incentives that can help deliver multi-species at risk conservation on Crown agricultural lands in Saskatchewan. We outline a range of policy instruments and incentives that are relevant to conservation on Crown agricultural lands and argue that a portfolio of options will have the greatest social acceptability. More germane is the need to foster collaboration between the government of Saskatchewan, other provincial/territorial governments, and the federal government, nongovernmental organizations, and land managers. Such collaboration is critical for enhanced decision-making and institutional change that reflects the urgent call for creating awareness of species at risk policies, building trust, and leveraging the local knowledge of land managers for conservation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

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)0.0010.001

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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.

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

Citations14
Published2021
Admission routes3
Has abstractyes

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