Bibliographic record
Abstract
La littérature est un texte, un tissu de relations de soi à soi, de soi aux autres, d’innombrables autres passés et à venir, par le biais du langage. Cet outil premier semble appartenir à tous, mais en réalité il n’est pas à la portée de chacun. Il serait donc intéressant de relater l’expérience angevine que je viens de vivre avec Lire~Ecrire~Compter (LEC), une association pour la promotion des savoirs, l’insertion sociale et professionnelle, créée en 1986, qui lutte contre l’illettrisme. L’une de ses approches originales, la “lecture-plaisir”, consiste à proposer à des volontaires de participer à la création d’un livre. Depuis 2004, l’association permet à ses “apprenants” de rencontrer un écrivain reconnu afin de participer à des ateliers d’écriture. Le fruit de leur travail commun est retranscrit dans la première partie de l’ouvrage publié ; la seconde nait de la libre créativité de l’auteur. Deciphering one’s life and writing it Literature is a text, a fabric of relations between oneself and oneself, between oneself and others – countless past and to come –, by the means of language. This tool is supposed to belong to all, but is in fact not accessible to everybody. In this respect, an experience I just went through in Angers (France) is highly interesting. Lire-Écrire-Compter (Read-Write-Count), an association for the promotion of knowledge, social and professional inclusion, and against illiteracy since 1986, proposes to its students to take part in the creation of a book. Since 2004, this program called “Reading-pleasure” offers students the opportunity of a “writing workshop” with a renowned writer. The first part of the published book presents this collective work; the second part originates from the author’s own creativity.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".