QUAND LE RÉEL SE TRANSFORME EN FICTION. LE CAS DE "LA BIEN-AIMÉE DE KANDAHAR" DE FELICIA MIHALI
Bibliographic record
Abstract
When Reality Changes into Fiction: Felicia Mihali’s La bien-aimée de Kandahar [The darling of Kandahar]. Writers often write their works using different aspects borrowed from reality. La bien-aimée de Kandahar written by Felicia Mihali is a novel that seems to be based on a real fact, a story between two young Canadians, Kinga Ilyes, a young woman of Romanian origin, and Christos Kariginnis, a young soldier of Greek origin. This sequence published in the magazine Maclean’s was borrowed from reality by the author and transformed in a real literary work. In this article, we aim at measuring the degree of fictionality of the text. In order to achieve this, we will first search for the referential indices at a paratextual level and then analyse the borrowings from reality made by the author of this text, as well as their fictionalisation. REZUMAT. Când realul se transformă în ficțiune. Cazul romanului La bien-aimée de Kandahar [Iubita din Kandahar] de Felicia Mihali. Pentru a-și scrie operele scriitorii se inspiră deseori din realitate. Romanul Feliciei Mihali, La bien-aimée de Kandahar [Iubita din Kandahar], pare să fie inspirat dintr-un fapt real, o poveste între doi tineri din Canada: Kinga Ilyes, de origine română, și Christos Karigiannis, un soldat canadian de origine greacă. Autoarea a împrumutat această istorisire din paginile revistei Maclean’s și a transformat-o într-o operă literară în sine. În acest articol, ne propunem să măsurăm gradul de ficționalitate al romanului analizat. În acest sens, ne vom îndrepta mai întâi atenția asupra paratextului și vom căuta indicii ale referențialității, urmând ca mai apoi să descoperim la nivelul textului împrumuturile din lumea reală pe care le-a făcut scriitoarea și modul în care acestea au fost transpuse în ficțiune. Cuvinte cheie: (non)ficțiune, împrumuturi din lumea reală, referențialitate, ficționalizare, istori(e)sire.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 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) | 0.006 | 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".