La asociatividad: Factor clave para el progreso en la microrregión del Río Mayo
Bibliographic record
Abstract
En el marco del Proyecto DRET: Desarrollo Rural con Enfoque Territorial en 3 Microrregiones de Cauca y Narino, auspiciado por la Organizacion Internacional para las Migraciones (OIM) y la Embajada de Canada en Colombia, y gracias a la invitacion del Dr. Edgar Jimenez Marulanda, Coordinador Regional de la OIM Narino, y de la Dra. Ana Victoria Munoz Mora del programa Migracion y Ruralidad, los magister: Javier Figueroa de la Maestria de Administracion y Competitividad, y Byron Castro Paz de la Especializacion de Alta Gerencia de la Universidad Mariana, se llevo a cabo el taller Micro Regional, realizado el dia 19 de marzo en el municipio de Colon-Genova, el cual, estuvo enfocado en la busqueda y desarrollo de propuestas con enfoque de territorio que ayuden a construir iniciativas economico-productivas, para la articulacion de las comunidades pertenecientes a la microrregion del Rio Mayo, y que agrupan municipios como: San Bernardo, El Tablon, San Jose de Alban, San Pablo, La Cruz, La Union, Belen, Colon-Genova, entre otros.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".