Socioeconomic Analysis of Rural Credit and Technical Assistance for Family Farmers in the Transamazonian Territory, in the Brazilian Amazon
Bibliographic record
Abstract
In Brazil, Rural Credit and Technical Assistance policies for family farming were formulated with the goal of promoting rural development in a sustainable and integrated manner. This study is the result of the Monitoring and assessment of public policies for territory management in the Pará Amazon project, undertaken by the Federal University of Pará (UFPA), aimed to evaluate the main socioeconomic impacts and limitations for the execution of these policies in the Transamazonian Territory. It is characterized as qualitative and exploratory, developed from bibliographic research and field research, based on data obtained through interviews conducted with 22 families of farmers who are beneficiaries of Rural Credit, the B modality of the National Programme for Strengthening Family Agriculture (PRONAF) and of the Technical Assistance Policy, whose sample corresponds to 10% of total contracts made effective within that Territory, between the years of 2013 and 2014. In addition to these farmers, for the analysis of the Technical Assistance service, interviews were conducted with extension workers from eight organizations, one of which is a state public company and seven of which are outsourced companies hired by the Federal Government to provide this service. The descriptive analysis shows that PRONAF B focuses on areas that produce short cycle food crops and on fishing activities. The technical assistance service provided by the public company is carried out in all the cities within the Territory, but only meets 10% of the demand; the service provided by the outsourced companies also occurs in all cities and its greatest setback is the delay in the release of funds by the Federal Government, which generates delays in the agricultural calendar and discontinuity in the productive activities, due to the end of the term of the companies’ contracts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".