Bibliographic record
Abstract
Ce n’est qu’après avoir su distinguer d’une part entre honte et culpabilité et d’autre part entre honte et pudeur, qu’il est possible d’explorer les raisons pour lesquelles la honte est l’affect qui accompagne avec une fréquence particulière les affections de la peau. Cette étape est essentielle car elle conditionnera l’abord thérapeutique. À partir des travaux de Didier Anzieu sur le moi-peau, on constate qu’il existe des liens très étroits entre la peau et le moi, de sorte que toute imperfection cutanée vient révéler au regard d’autrui ce qui devrait rester caché, c’est-à-dire la part intime souffrante de soi, ce qui provoque la honte. Soigner la peau nécessite de prendre en compte cette souffrance psychique, révélée et / ou provoquée par les troubles cutanés.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.028 |
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; both teacher heads agree on what is shown here.
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".