EDUCACIÓN LATINOAMERICANA: RETOS DE CONSTRUCCIÓN SOCIO-ESPACIAL
Bibliographic record
Abstract
There are two elements that must be reflected upon from education in their duty of caring about its social responsibility; its relationship to prevailing market logic and recognition of the “other” within the socio-spatial relationships that shape the educational act. Then, a thoughtful article (Palou, 2001) that emerges from approaching Torres (2009), Laval (2004) and Castro’s (2000) statements where a dialogue about the impact of the relationship of the social knowledge and education seeking to build a historical consciousness as an element to rescue the Being is proposed, from the recognition of socio-spatial realities, for which at a first moment a historical balance of education and socio-spatial realities relationship is developed; this in order to show the bond between them and their impact on social movements. In a second moment, the problem of colonization processes in the world and particularly in the region from the Rio Bravo in Mexico to Cape Horn in Chile, Latin America is placed in evidence. Finally, the impacts of educational policies were highlighted, result of the logics of social knowledge developed from external sites to the Latin American reality and its impact on the understanding of education. In the end, some specic re‑ections as a conclusion that highlight the importance of rebuilding the Latin American University from the recognition of socio-spatial realities are presented.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".