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Record W3156316507 · doi:10.1080/21683565.2020.1870645

Why is grazing management being overlooked in climate adaptation policy?

2021· article· en· W3156316507 on OpenAlexafffundabout
Wesley Tourangeau, Kate Sherren

Bibliographic record

VenueAgroecology and Sustainable Food Systems · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrazingClimate change adaptationGeographyAdaptation (eye)Crop managementClimate changeEnvironmental resource managementPolitical scienceAgroforestryEnvironmental planningAgricultureEconomicsEcologyEnvironmental sciencePsychologyBiologyArchaeology

Abstract

fetched live from OpenAlex

In 2018, two studies were conducted by Canada’s Parliament on the connections between climate change and agriculture. Links between grazing management and climate change adaptation and mitigation are included in the testimonies gathered during these studies but the resulting final reports are silent on the topic. Analysis of 112 parliamentary files revealed insights on (1) the knowledge about grazing management that was omitted from the two final reports, (2) the social contexts that informed the processes of hearing testimonies and developing the reports, and (3) the underlying ideologies and normative assumptions reflected in these studies. Overall, the current state of policy regarding climate change and agriculture emphasizes technical, scientific, and expensive solutions, and as a result, the benefits of grazing management are overlooked. We argue transformations toward sustainable, climate adaptive agriculture require an ongoing examination of how political structures, knowledge hierarchies, and underlying ideologies inform and narrow policy outcomes.

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 score0.463
Threshold uncertainty score0.585

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations4
Published2021
Admission routes3
Has abstractyes

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