Interspecies relationships and reimagined futures in the speculative fiction of Margaret Atwood and Waubgeshig Rice
Bibliographic record
Abstract
Ce mémoire repose sur des lectures approfondies de The Year of the Flood (2009) de Margaret Atwood et Moon of the Turning Leaves (2023) de Waubgeshig Rice afin d’examiner la représentation des relations interspécifiques dans la fiction spéculative canadienne. Les deux romans posent aux lecteurs des questions de la vie à la suite d’une apocalypse environnementale et envisagent de nouvelles méthodes de « world-building » comme forme de survie. Cette survie repose sur la remise en question des systèmes coloniaux, capitalistes et hétéropatriarcaux à la base de l’assujettissement des femmes et de la dégradation de l’environnement dans les sociétés occidentales. S’appuyant sur la théorie écocritique d’Anna Tsing et Greta Gaard, ainsi que sur les oeuvres féministes de Catherine Rottenberg et les concepts de décolonisation d’Ingrid R. G. Waldron et Sylvia Wynter, ce mémoire explore les intersections de la violence envers les femmes et la nature tout en examinant comment les médias accessibles, comme la littérature populaire, peuvent inspirer de nouvelles approches envers l’Anthropocène qui rejettent les structures hiérarchiques d’oppression préexistantes.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".