How can agricultural extension and rural advisory services support agricultural innovation to adapt to climate change in the agriculture sector?
Bibliographic record
Abstract
Because the climate has been rapidly changing and undermining the sustainability of the agriculture sector, Agricultural Extension and Rural Advisory Services (AERAS) need to rethink their contemporary roles and initiatives. Although enhancing agricultural innovation is considered a key process to increase farm income and ensure sustainability under complex climate-affected development conditions, little is known how AERAS can support the process in the said context. A broad range of literature was reviewed and a deductive coding approach was followed to analyze the literature. The findings suggested numerous transformative roles of AERAS providers supporting agricultural innovation. AERAS providers should extend their mandates and broaden their scopes by connecting and working with multiple actors and groups within and beyond the agriculture sector. They need to support interactions and learning among diversified actors to develop complementary understanding and approaches for collective action for climate change adaptation. The findings highlight the importance of enhancing innovation by AERAS providers for climate change adaptation in the agriculture sector.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".