Mireille Desjarlais-Heynneman, 27th Annual ODU Literary Festival
Bibliographic record
Abstract
Mireille Desjarlais-Heynneman was born in Montréal (Québec), Canada. She holds degrees from the Université de Montréal (Social Work), and from La Sorbonne, Paris (Literature). Mireille is a founding member of the Société des écrivains de Toronto (French writers of Toronto), and has been active in the organization of Le Salon du livre de Toronto (Toronto French Book Fair) since its creation in 1992, for which she also served as vice-president for several years. Publications include Le Bestiaire (poems on Mirca Delanoé’s drawings), Autour de Paul Savoie, poems and short stories published in various collections and literary publications in Canada and Luxembourg; forthcoming works include one poetry manuscript and one children’s story, soon to be published in Toronto and Ottawa, respectively; and Les mémoires littéraires de Laure Rièse (the literary memoirs of Laure Rièse). Mireille’s awards include Laureate for individual poems in Québec, and for a short story in the magazine Stop (Montreal), and Special Mention from the International Concours de poésie de la ville d’Arles, France. Mireille is a member of the AAOF (French writers of Ontario) and the SPF (Society of French Poets, Paris).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 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".