L’apport de l’anthropologique à la perception contemporaine de l’humain
Bibliographic record
Abstract
Dans ce texte, les auteurs ont pour objectif d’extraire l’image de l’humain qui se dégage de l’expérience anthropologique. Ils concluent que l’image de l’humanité est multiple et variante, donc que l’espoir d’une vision unique est utopique. En fait, on peut s’attendre à ce qu’elle se modifie encore dans l’inexorabilité de l’histoire. Les postulats de l’évolutionnisme permettent d’entrevoir une accélération du changement. ------ In this paper, the authors make an attempt to extract the image of humans that emerges from the anthropological experience. They conclude that this image of humanity is multiple and variant, so that the expectation of finding a unified vision is utopian. In fact, we can predict that it will change again due to the necessity of history. The assumptions of evolutionism presume an accelerated change. ------ Neste artigo, os autores pretendem extrair a imagem do ser humano que emerge da experiência antropológica. Eles concluem que esta imagem da humanidade é múltipla e variável, pelo que a esperança de encontrar uma visão única é utópica. Na verdade, podemos prever que ela ainda vai mudar devido à inevitabilidade da história. Os postulados do evolucionismo presumem uma mudança acelerada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".