Cuando la universidad de afuera entra: ¿Cuáles son las características claves para promover la investigación participativa?
Bibliographic record
Abstract
En este artículo, analizamos la pregunta: ¿Cuáles son las características claves para una alianza entre una universidad de afuera y actores locales para promover la investigación participativa? Utilizamos la escalera de participación (Briggs, 1989) y el continuo interno-externo de la reseña histórica (Herr y Anderson, 2005) para analizar ejemplos de investigación hecho por universidades de afuera en Waslala, Nicaragua. Argumentamos que investigadores de afuera deben aliarse con un actor local para mejorar y garantizar la participación en todas las etapas del proceso de investigación - formulando la pregunta de investigación, la recopilación de datos, el análisis y la diseminación de resultados. Características claves para facilitar participación en cada etapa incluyen: la presencia de mediadores culturales, oportunidades para comunicación y diseminación en múltiples idiomas y usando diferentes medios para alcanzar con audiencias distintas, y que un proyecto sea parte de una alianza con fines más allá qué sola investigación.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".