Bibliographic record
Abstract
Toute quête identitaire nous conduit forcément à un questionnement sur nos origines. Qu’est-ce qui nous distingue de nos proches? Quels sont nos premiers traits humains apomorphes? Quelle est la première instance de notre humanité? Plus généralement, en tant qu’êtres vivants, de quelle manière sommes-nous propulsés vers l’humanisation? La trajectoire des hominides nous parait ici centrale. Si, à l’évidence, l’ensemble des sciences humaines s’y intéressent, la sémiotique intervient principalement pour questionner notre phéno-réalité dans sa dimension biosémiotique (voire aussi paléosémiotique, archéosémiotique, etc.) Notre travail consiste ici à relever les éléments propres à cette trajectoire. Pourra-t-on mieux comprendre nos traits singuliers en étudiant leur inimitabilité? La robotique humanoïde, toujours incapable malgré ses prétentions de reproduire l’être humain, pourrait-elle nous aider à y voir plus clair? En opposant biosémiose et robosémiose, cet article propose de faire un pas dans ce débat, dont l’enjeu consiste à déceler en nous le trait humain le plus significatif et à saisir comment l’inégalable summum de notre humanité pourrait enfin se dévoiler.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".