Realizando evaluaciones de necesidades de capacidades funcionales. Manual para instructores
Bibliographic record
Abstract
Este manual fue producido como un recurso para la capacitación de Facilitadores Nacionales de Innovación (FNI) en los ocho países piloto. La capacitación está destinada a ser impartida por la Persona Focal de Agrinatura (PFA) y los Gerentes Nacionales del Proyecto (GNP) en cada país. Estos instructores han pasado por un proceso de capacitación para instructores para familiarizarse con este manual, el enfoque interactivo y participativo requerido y el uso de las diversas herramientas de facilitación que se encuentran en él. El objetivo de este manual es fortalecer las habilidades de facilitación de los FNI y su capacidad para apoyar a los agricultores y otras partes involucradas en analizar los problemas clave que enfrentan, crear una visión de donde ellos quieren estar, y construir la apropiación de este largo proceso. Este manual es adecuado para ser usado por ONGs, departamentos gubernamentales y universidades para desarrollar las capacidades del personal para planear y ejecutar intervenciones de acuerdo con las necesidades de los participantes esperados.
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.
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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".