The classroom as a space of resistance. Cooperation, gratitude and collective memory between neuroscience and social science
Bibliographic record
Abstract
This article explores social transformation through pedagogy, historical consciousness, social science, and neuroscience. Modern educational systems perpetuate social hierarchies. The meritocratic narrative of neoliberalism is a new form of social Darwinism. Behind this illusion lie the mechanisms of accumulated history, social reproduction, and the inheritance of economic, social, cultural, and symbolic capitals. Collective memory is one of these capitals, cultivated by the elites. In contrast, the memory of the vanquished fades into oblivion. Therefore, democratic pedagogy aims to build collective memory and a historical consciousness of equality, inequality, and human rights. This project requires cognitive and methodological tools for both teachers and students. Our proposal is both theoretical and practical. Theoretically, we aim to build a historical awareness and resilience capacities to address the algorithmic colonization. Cooperative pedagogy and neuroscience bring constructive tools. This approach fosters a new rationalism and complex thinking that unifies natural, social, and human sciences into a cohesive pedagogical praxis. We propose to build collective memory and historical awareness among students, pedagogical team, and families. This involves teacher training, an emotional and prosocial climate, cooperative skills, historical research teams, collecting of family memories, and collective synthesis. The project fosters social bonds and skills for democratic sovereignty. Methodologically, the research employs a critical bibliographical review and content analysis from CAIRN, OpenEdition, ScienceDirect, Web of Science, ERICH+, EBSCO, Scopus, and Google Scholar. The selected bibliography includes authors from Canada, Chile, England, France, Germany, India, Switzerland, the Philippines, and the United States.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".