Reducción de la desnutrición crónica infantil y participación de la sociedad civil del distrito de Lonya Grande 2020
Bibliographic record
Abstract
The present research "Reduction of Chronic Child Malnutrition and Participation of Civil Society of the District of Lonya Grande. 2020" aims to analyze the participation of civil society in reducing chronic child malnutrition. It arises as a need to find solutions to the increase of this problem in the district of Lonya Grande (Utcubamba/Amazonas) that despite development actions in the area has not been able to reduce inequities in the health of the vulnerable population. \nThe development of the research revolved around the implementation of the project Utilization of the coffee pulp by-product of the district of Lonya Grande - Amazonas as a new food alternative to combat malnutrition and anemia in vulnerable populations of Peru (Record 60176), in which the roles, benefits and interests of participating civil society organizations (CSOs) were analyzed in order to describe their relationship with the reduction of chronic child malnutrition. \nThe results show that there is a relationship between reduction of chronic malnutrition and shared participation among the three CSOs involved in the project. This is demonstrated in each of the functions, benefits and interests analyzed during the elaboration, execution and closure of the project, materialized in four publications that gave rise to a collaborative network with four CSOs from Canada, the United States, Ecuador and Colombia. \nIt is concluded that the participation of CSOs has exceeded expectations improving anemia levels in children under 8 years. It also highlights the main role of the UNT, through its agent of change, throughout the project implementation process. Also, because there are no experiences of similar projects with a high degree of technological maturity, added to the disparity of interests at the completion of the project, technology transfer was limited.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".