Transformative adaptation: from climate-smart to climate-resilient agriculture
Bibliographic record
Abstract
Abstract In response to the climate crisis, there has been much focus on climate-smart agriculture (CSA); namely, technologies and practices that enhance adaptation, reduce greenhouse gas emissions, and contribute to food security; the so-called triple win. Success has tended to be measured in terms of the number of farmers adopting CSA with less focus given to the impacts especially on human development. CSA can inadvertently lead to 'maladaptation' whereby interventions reinforce existing vulnerabilities either by benefitting powerful elites or by transferring risks and exposure between groups. Such maladaptive outcomes often stem from overly technical adaptation programming that is driven by external objectives and discounts the social and political dynamics of vulnerability. Increasingly a more nuanced picture is emerging. This reveals how a failure to contextualize CSA in relation to the structural socio-economic dynamics associated with agricultural systems that render some categories of farmer especially vulnerable to climate change, undermines CSA's contribution to reducing rural poverty and increasing equity. In response, there is a growing focus on transformative orientations that pursue a more deep-seated approach to social, institutional, technological and cultural change in order to address the structural contributors to vulnerability and differential exposure to climate risk. Addressing these questions requires a robust consideration of the social contexts and power relations through which agriculture is both researched and practiced. For agriculture to be transformative and contribute to broader development goals, a greater emphasis is needed on issues of farmer heterogeneity, the dangers of maladaptation and the importance of social equity. This entails recognizing that resilience encompasses both agro- and socio-ecological dimensions. Furthermore, practitioners need to be more cognizant of the dangers of (i) benefiting groups of already better off farmers at the expense of the most vulnerable and/or (ii) focusing on farmers for whom agriculture is not a pathway out of poverty. The success of these approaches rests on genuine transdisciplinary partnerships and systems approaches that ensure adaptation and mitigation goals along with more equitable incomes, food security and development. The greater emphasis on social equity and human well-being distinguishes climate-resilient from climate-smart agriculture.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.009 |
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 teacher head, 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".