Farm-Level Adaptation to Multiple Risks: Climate Change and Other Concerns
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.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it