Soft adaptation: The role of social capital in building resilient agricultural landscapes
Bibliographic record
Abstract
The resilience of agricultural production is perpetually challenged by a wide range of disturbances from the impacts of climate change, to political instability and urbanization. At the same time, agriculture production also depends on relatively stable socio-ecological conditions to ensure quality and yield. Understanding how producers in agricultural landscapes can increase adaptive capacity, and remain resilient in the face of these challenges has become a priority for farmers, for researchers and national political agendas on a global scale. The current state of knowledge on adaptation tends to focus overwhelmingly on “hard” adaptation, such as infrastructure and technological inputs, rather than “softer” strategies, such as agroecological management or social capital, which are less easily measured. This research aims to explore soft strategies for adaptive capacity, in particular, the effect of social capital on the adaptive capacity of agricultural systems, using a case study of the agricultural landscape in the Okanagan Bioregion. The findings suggest that soft adaptation is a vital strategy for cultivating agricultural resilience, and underpins the ability of producers to use other soft and hard adaptation strategies. Participants in this research highlighted the importance of social connection, networks, reciprocity, learning and knowledge transferral, as key tools used to increase their adaptive capacity. They also highlight social capital as a building block for other forms of capital, such as financial, physical and environmental capitals. Despite this importance of soft adaptation, participants also indicated that they would be more likely to focus on implementing “harder” strategies that respond more directly and tangibly to key disturbances, rather than “soft” strategies. These results suggest a contradiction between the importance and value that producers place on social capital and “soft” adaptation, and the strategies they actually plan to implement. Further research is required to understand this contradiction, and to explore how to communicate the value of “soft” adaptation to producers in a way that makes the benefits more concrete and observable, and allows them to capitalize on the currency of connection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".