Bibliographic record
Abstract
Le mot « accompagnement » s’est largement diffusé et popularisé au cours dernières années pour qualifier des pratiques d’intervention auprès de publics variés, dans des secteurs les plus divers — malades, sans-abri, élèves en difficultés, immigrants, etc.. À partir d’entrevues réalisées en France auprès d’intervenants oeuvrant dans quatre secteurs — soins palliatifs, soins aux personnes âgées, éducation, insertion au travail —, nous avons cherché à savoir ce que le mot accompagnement désigne et à dégager ce que ces pratiques ont en commun et ce qui les distingue. Nous avons ainsi mis en évidence le socle idéologique commun à des pratiques d’accompagnement par ailleurs très différentes. Cela nous a également permis de clarifier quelques-uns des enjeux posés par les transformations actuelles de l’intervention psychosociale.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".