The adaptive capacity of institutions in Canada, Argentina, and Chile to droughts and floods
Bibliographic record
Abstract
The increasing evidence of global warming calls on all states to enhance their adaptive capacity to deal with climate change. This paper compares the adaptive capacity of two Canadian provinces, the province of Mendoza, Argentina and the administrative region of Coquimbo, Chile in relation to the vulnerability of farmers to droughts and floods by applying the adaptive capacity wheel (ACW). It concludes that Saskatchewan and Alberta, Canada are particularly weak in terms of double- and triple-loop learning and in developing adaptive capacity in an equitable manner, probably attributable to strong climate scepticism in society and the weak economy. In the developing countries of Chile and Argentina, resources to assist with adaptation are often lacking; in Coquimbo, future learning is precarious because of information deficits in relation to data, memory, trust, and responsiveness; in Mendoza, institutions lack variety (redundancy of programs), resources, and governance processes are inadequately responsive. The paper makes contributions at the regional level by recommending that specific institutional weaknesses and lack of responsiveness be remedied by adopting appropriate missing instruments (perhaps, for example, water transfer provisions in Mendoza). New findings are made in relation to the dimensions of fair governance and learning capacity in the ACW. While learning capacity was closely linked to the dimension of leadership, the deficit of equity was closely linked to other indicators of fair governance (legitimacy, responsiveness, and accountability).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".