Las Comunas costeras del Ecuador frente al COVID-19 : alianzas sociales, territorio, autonomía y dependencia
Bibliographic record
Abstract
Desde marzo hasta mayo de 2020, el número de fallecidos por causa del COVID-19 creció drásticamente en las Comunas de Santa Elena. Surgiendo así un panorama de desesperación, inseguridad y precariedad monetaria, que dura hasta el momento. A pesar del descenso turístico, el desempleo y la hambruna, surgió también un escenario caracterizado por las acciones comunitarias y las alianzas sociales, y con ello, se produjo una reconexión con el territorio como espacio sociocultural, pilar central que sostiene el proyecto comunitario. La información aquí expuesta es el resultado de una base de datos recopilada durante estos meses, con el objetivo de visibilizar la situación de las 70 Comunas de Santa Elena durante el estado de emergencia. Además, la metodología usada para compartir los datos con los dirigentes comunales invita a pensar en nuevos métodos dentro del contexto académico.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".