Bibliographic record
Abstract
All the Feels / Tous les sens presents research into emotion and cognition in Canadian, Indigenous, and Québécois writings in English or French. Affect is both internal and external, private and public; with its fluid boundaries, it represents a productive dimension for literary analysis. The emerging field of affect studies makes vital claims about ethical impulses, social justice, and critical resistance, and thus much is at stake when we adopt affective reading practices. The contributors ask what we can learn from reading contemporary literatures through this lens. Unique and timely, readable and teachable, this collection is a welcome resource for scholars of literature, feminism, philosophy, and transnational studies as well as anyone who yearns to imagine the world differently. Contributors: Nicole Brossard, Marie Carrière, Matthew Cormier, Kit Dobson, Nicoletta Dolce, Louise Dupré, Margery Fee, Ana María Fraile-Marcos, Smaro Kamboureli, Aaron Kreuter, Daniel Laforest, Carmen Mata Barreiro, Ursula Mathis-Moser, Heather Milne, Eric Schmaltz, Maïté Snauwaert, Jeanette den Toonder
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".