The Assessment of Three Measures (101, 103, 302) Under the National Plan of Agriculture and Rural Development of Kosovo
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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