Bibliographic record
Abstract
On admet en général qu’il y a deux sortes de concepts normatifs : les concepts évaluatifs, comme bon , et les concepts déontiques, comme devoir . La question que soulève cette distinction est celle de savoir comment il est possible d’affirmer que les concepts évaluatifs sont normatifs. En effet, comme les concepts déontiques semblent constituer le coeur du domaine normatif, plus le fossé entre les deux sortes de concepts est grand, moins il paraîtra plausible d’affirmer que les concepts évaluatifs sont normatifs. Après avoir présenté les différences principales entre les concepts évaluatifs et les concepts déontiques, et montré qu’il y a plus qu’une différence superficielle entre les deux sortes de concepts, j’examinerai la question de la normativité des concepts évaluatifs. Il deviendra apparent que même s’il s’agit de concepts ayant des fonctions différentes, il existe un grand nombre de relations entre les concepts évaluatifs, d’une part, et les concepts de devoir et de raison, d’autre part.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".