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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it