Fiction de soi et fiction d'autrui dans «Marc Beltra, roman autour d'une disparition » de Mathieu Simonet suivi du texte de création «Je suis Elles»
Bibliographic record
Abstract
This thesis studies how fictions – of oneself and others – can sometimes make reality more believable. Through the study of the novel Marc Beltra, roman autour d’une disparition, we will discover the approach of writer-lawyer Mathieu Simonet as well as the concept of "collective autobiography". A hybrid genre inspired by Philippe Lejeune’s autobiographical pact and Serge Doubrovsky’s autofiction revisited by Philippe Gasparini, Simonet’s collective autobiography is a new way of "telling lives" while taking into consideration the ethical and legal issues of our time. The author presents a collage of testimonies, extracts from diaries and personal thoughts in the form of a fragmented writing seeking to produce a literature that is both useful and therapeutic. Simonet’s approach can be explained in the text as well as in the paratext, and this allows his works to become – beyond the books – real performances. To do this, the author establishes several "pacts" with his reader, his publisher, but also with his characters. This approach, in its ethical dimension, inspired the creative text Je suis Elles. From interviews with seven women with very different backgrounds, a common story emerges around the issue of motherhood. Combining reality and fiction in the service of storytelling, Je suis Elles proposes a universal portrait of today’s woman – from the point of view of the character of Louise, a French student in Montreal who is about to submit her thesis in research and creative writing at McGill University.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".