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Record W2244389539

Farm-Level Adaptation to Multiple Risks: Climate Change and Other Concerns

2008· article· en· W2244389539 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of rural and community development · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsBrandon University
Fundersnot available
KeywordsClimate changeVariety (cybernetics)Adaptation (eye)Context (archaeology)Climate change adaptationPoliticsEnvironmental resource managementPolitical economy of climate changeEnvironmental planningNatural resource economicsPolitical scienceRegional scienceGeographyEconomicsPsychologyComputer scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

The impacts of, and responses to, climate change have been of recent interest to social scientists. The purpose of this paper is to present results from a case study examining farm-level adaptation, within the relevant social, political, and economic context, to risks and opportunities presented by climate change in one region of Manitoba, the Parkland region. This was pursued by soliciting opinions and impressions from farmers in the Parkland region of Manitoba regarding a variety of questions relating to previous and future farm-level adaptations to multiple risks and opportunities with a particular emphasis on climate change. The paper begins by drawing upon the research literature in developing a model of farm-level adaptation. This model is then applied to the Parkland region in Manitoba through a survey of farmers in the region.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.446

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 studies0.0010.000
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 head0.237
GPT teacher head0.283
Teacher spread0.046 · 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