The investment support capacity for technology of small farms and rural households in Western Romania
Bibliographic record
Abstract
This paper targets the developments concluded within specifically delimited areas of LEADER axis, namely selected territories of Local Action Groups. The reason of this choice is related to the potentially large physical and economic size of the farms and households where the funding availability for co-financing and supporting the non-eligible expenditure is different to the small and medium farms and agricultural households. The aim of the analysis is to find and quantify the capacity of the small farming operations which are usually retrieved in LAGs where their relative small projects have a better chance to contract the public support compared to the national competition of the Programme (NRDP). The present analysis selects LAG territories from different geographical regions of Romania with different cultural, entrepreneurial and agricultural background and quantifies and compares the private contribution at the scale of the territories in order to check the assumption that the supporting capacity of small farms and agricultural households is very limited. As the conclusions highlight the capacity is far from being reduced or small particularly when the scale of operations is relatively small. Also, the financial surroundings and the overall success of the implementation are the best public-private-partnership success story in Romania for the last century-quarter.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".