Las escritoras inmigrantes en Québec: labor de la memoria, voluntad de explotación / The writing by Inmigrant Women in Québec: Work of Memory Construction, will of Exploration
Bibliographic record
Abstract
RESUMEN Este artículo analiza el campo de la escritura de mujeres inmigrantes en Québec. Tras una primera parte, que aborda el campo de la literatura de inmigración en Québec, desde el punto de vista de la creación, de la recepción y de la investigación, la segunda parte plantea la especificidad de la escritura de mujeres inmigrantes y la tercera parte profundiza en la obra de tres escritoras provenientes de horizontes diversos: Marie-Célie Agnant, Abla Farhoud y Ying Chen. Palabras clave: Québec, literatura de inmigración, escritura de mujeres inmigrantes, Marie-Célie Agnant, Abla Farhoud, Ying Chen. ABSTRACT This article describes the field of writing by immigrant women in Québec. The first part deals with writing by immigrants in Québec from the point of view of creation, reception and research. The second part sets out the specific features of the writing by immigrant women. Finally, the third part goes into depth in the work of three women with very different backgrounds: Marie-Célie Agnant, Abla Farhoud and Ying Chen. Key words: Québec, literature of inmigrant women, the writing by inmigrant women, Marie-Célie Agnant, Abla Farhoud, Ying Chen.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".