The institutional support index: A pragmatic approach to assessing the effectiveness of institutions' climate risk management support-A case study of farming communities in Pakistan
Bibliographic record
Abstract
The effects of climate change are global and will worsen in the future. People face uncontrollable large-scale events due to the crisis. To manage climate-induced risks, understanding all threats is crucial. A country's governance system is responsible for risk management. Pakistan is highly vulnerable to climatic disasters, making its governance system crucial. To achieve climate risk resilience, farmers need tailored institutional services. This study investigates the efficacy of such services in Punjab province, Pakistan. Four hundred eighty farmers in Punjab's mixed cropping zone were interviewed face-to-face using a predesigned structured questionnaire to collect data on five types of institutionally provided services (e.g., weather and climate forecasts, farm advisory, financial services, technical support, and training). Institutional support for climate risk management is assessed using an indicator-based index approach by selecting four indicators/dimensions reflective of service effectiveness (e.g., content coverage, service accessibility, compatibility, and usefulness). The survey results showed that farmers had varying perceptions of institutional services, with low-medium levels of support and fair content coverage, accessibility, and usefulness. Most services lacked compatibility. Researchers recommend improving agricultural service compatibility to build farming communities' resilience to climate risks.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".