Bibliographic record
Abstract
Neste texto compara-se a disciplinaridade, a interdisciplinaridade, a multidisciplinaridade e a transdisciplinaridade e procura-se captar os vínculos entre tais abordagens e a interculturalidade, o monoculturalismo plural, o monoculturalismo e a transculturalidade no contexto das Américas a fim de medir o grau de possibilidade de inclusão do terceiro incluído favorecido pela reflexividade e a recusa do nacionalismo metodológico que leva ao dualismo e à exclusão.Résumé: On compare la disciplinarité, l’interdisciplinarité, la multidisciplinarité et la transdisciplinarité et on essaye de saisir les liens entre ces approches et l’interculturalité, le monoculturalisme pluriel, le multiculturalisme et la transculturalité dans le contexte des Amériques afin de mesurer le degré de possibilité d’inclusion du tiers favorisé par la réflexivité et le rejet du nationalisme méthodologique aboutissant au dualisme et à l’exclusion.Mots-clés: multidisciplinarité; multiculturalisme; Amériques; réflexivité; nationalisme;tiers inclus.Abstract: In this text, disciplinarity, interdisciplinarity, multidisciplinarity and transdisciplinarity are compared. Then, one establishes links between these approaches and interculturality, plural monoculturalism, multiculturalism and transculturality in the context of the Americas. This leads to evaluate the possibility to escape from the logic of the excluded third thanks to the practice of reflexivity and the rejection of methodological nationalism linked to dualism and exclusion.Keywords: multidisciplinarity; multiculturalism; Americas; reflexivity; nationalism;included third.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".