The Evaluation of Rural Development Policy: Macro and Micro Perspectives L’évaluation de la politique de développement rural: perspectives macro et microéconomiques Die Evaluation der Politik zur Entwicklung des ländlichen Raums: Mikro‐ und Makroperspektiven
Bibliographic record
Abstract
Summary The Evaluation of Rural Development Policy: Macro and Micro Perspectives Effective rural development (RD) policy requires transparent goals, specific objectives, well‐defined metrics to measure success, and rigorous evaluation to justify sound policy. Such evaluation can employ aggregate indicators of impact (a macro approach) and/or more disaggregated information (a micro approach). Each has its place. Changes in population and population structure provide a key macro indicator of the success of rural development policy, since individuals reveal the attractiveness of rural areas by ‘voting with their feet’. By using additional data on the factors that attract people to rural areas, the targeting of rural development policy can be improved. Policymakers frequently need to examine specific policy initiatives in detail to determine their impact, if any, and how and why they achieve their effects. The example of farm diversification policy in the EU illustrates that information obtained from case studies can help not only to elaborate the impact of policy but also illuminate how that impact is generated. Without complementary in‐depth inquiry, scope for making sense of quantitative indicators, which have been the primary focus of evaluation in the EU, is limited; but without a broad base of measurement, the usefulness of insights derived from case study analysis is also restricted.
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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 | 0.043 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".