The Assessment of Three Measures (101, 103, 302) Under the National Plan of Agriculture and Rural Development of Kosovo
Bibliographic record
Abstract
Summary Subject and purpose of work: Agriculture has historically been an important sector in Kosovo’s economy however the biggest challenges are migration, land fragmentation, and access to market and finance. Support from the Government of Kosovo for the agriculture and rural development sector is based on the ARDP 2007-13 and includes direct support measures that strongly correspond to Pillar I measures under CAP and rural development support measures similar to CAP Pillar II. The objective of this paper is to assess three measures (101,103,302) under the national plan of agriculture and rural development of Kosovo. Materials and methods: Measure 101, “Investments in Physical Assets in Agricultural Holdings” fruit sector, grape sector. Measure 103, “Investments in physical assets concerning the processing and marketing of agricultural and fishery products”. Measure 302, “Farm Diversification and Business Development”. Results: Results showed support is increased which directly affected new job creation however this should continue with increasing the budget as these measures affect the rural economy directly by creating jobs contributing to sustainable agriculture and reducing migration. Conclusions: The most important measure in terms of budget allocation and number of projects implemented was Measure 101. The largest number of beneficiaries from measure 101 originated from the Prizren and Prishtine Region.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".