Fuego de la memoria. Resonancias geográficas en la obra de Eduardo Galeano
Bibliographic record
Abstract
La muerte de Eduardo Galeano el 13 de abril de 2015 nos arrebato no solo a uno de los mas eminentes escritores de America Latina, sino a un ciudadano global de inmensa estatura, a un maestro inimitablemente versado en documentar los fatigados trillos del mundo, y sin rival en celebrar su miriada de maravillosas dichas. La participacion en un foro de homenaje a Galeano, organizado por la Fundacion Vivian Trias en su Montevideo natal en octubre de 2016, me brindo la oportunidad de revisar y adentrarme en su legado, parte del cual tiene, decididamente, resonancias geograficas, especialmente en relacion a la region del mundo que mas le importo: America Latina. Un pais en particular, Guatemala, evoca una pasion compartida por el autor y el celebre escritor, un entorno que influye como aprender y ensenar la geografia en el aula universitaria. The death of Eduardo Galeano on April 13, 2015 removed from our midst not only one of Latin America’s most eminent men of letters but a global citizen of immense stature, a master of his craft inimitably versed in documenting the world’s weary ways and unrivaled in celebrating its myriad, marvellous joys. Participation in a forum paying homage to Galeano, organized by the Fundacion Vivian Trias in his native Montevideo in October 2016, affords the opportunity to look back, take stock, and engage his legacy, part of which has a decidedly geographical resonance, especially in relation to the part of the world he cherished most: Latin America. One country in particular, Guatemala, evokes a passion shared by both the autor and the distinguished writer, a setting that influences how to learn about geography and instruct it in the classroom.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".