Bibliographic record
Abstract
Dear Cécile, I feel so honoured and privileged, as a former colleague and a close friend, to be able to initiate this dialogue across languages, cultures and continents with you. Since 1995, you have been publishing poems, short stories, novels and essays, while leading a career as Associate Professor of English at the University of Paris 12 and continue to bring out remarkable volumes of such creations, winning several prestigious awards on the way. However, I would like to single out my favourite text – A fleur de mots, La passion de l’écriture (2004) which is an incredibly beautiful essay on writing generally and your own creative process. I personally think that this book needs to be translated into English, without any further delay, for the benefit of the Anglophone audience worldwide. There you refer to the page as “this strange country of water and reflections” and describe your passion for words, specifying that there is “no special time for writing”. The need for space, “a room of one’s own”, seems to be still the preoccupation of many writers. At the same time, because of your diverse peregrinations (an artist mother who had lived in India, a maternal family in Canada, relatives in Belgium, and your husband’s roots in Tunisia, students in Finland), you have travelled to and discovered the history and memory of nations and peoples. Is Space or Time that stirs you most into writing?
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.171 | 0.089 |
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".