Contribution à une introduction aux études des systèmes hommes-machine de Michel Olivier
Bibliographic record
Abstract
Pour introduire cet article, je voudrais le situer dans une histoire en donnant quelques informations, d'une part, sur le milieu de recherche dans lequel il s'insre, d'autre part, en indiquant en quoi il est annonciateur de dveloppements qui vont suivre. Ces deux dmarches devraient contribuer faciliter l'intelligibilit de ce texte. En dmlant un peu ses sources, ses dterminants, on comprend mieux sa composition et ses aspects originaux. En examinant comment les problmes qu'il a relevs et essays de bien poser en rfrence un certain cadre thorique ont t traits par la suite, on donne l'article une autre source d'intelligibilit. En permettant d'apprcier dans quelle mesure les perspectives thoriques privilgies par l'auteur ont constitu un lment du progrs des connaissances sur l'objet tudi, les systmes hommes-machines, on donne, par l, ces perspectives une signification enrichie.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".