Facilitating learning for innovation in a climate-stressed context: insights from flash flood-affected rice farming in Bangladesh
Bibliographic record
Abstract
Purpose Facilitation of learning enhances innovation through overcoming innovation barriers and supporting learning outcomes. However, little is known about how public Extension and Advisory Services (EAS) facilitate learning to help adapt to particular climate stressors. This article investigates the role of public EAS in facilitating learning to enhance innovation in a flash flood-affected farming context.Design/methodology/approach The research adopted flash flood-affected rice farming in Bangladesh as a case and collected data with actors involved in various extension approaches using interviews and focus group discussions.Findings Public EAS should involve a range of relevant actors, including the private sector and scientists, and jointly evaluate with farmers and provide feedback on the effectiveness of various crop cultivation strategies for flash flood adaptation. Public EAS needs to deliver the necessary instrumental support and resources to achieve learning outcomes and enable farmers to make desirable changes to farm activities.Practical implications Policy makers need to develop policies for the capacity development of public EAS staff and provide adequate resources so that public EAS can facilitate learning approaches to support discussions on local concerns and the use of local knowledge, experiences, and resources for flash flood adaptation.Theoretical implications Facilitation of learning to support adoption of technological innovations is not sufficient in the context of flash flood adaptation. Facilitation should support discussions on effective utilisation of natural and common resources for the flash flooding context.Originality/ value The study investigated the ways public EAS can facilitate learning to overcome barriers to innovation and support learning outcomes in a flash flooding context.
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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 | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".