Conversation questions for Professors Anne Simon and Stephanie Posthumus
Bibliographic record
Abstract
These conversations highlight the ways in which Stéphanie Posthumus and Anne Simon, who work in different academic fields but often look at similar literary texts written in French, envision ecocriticism and zoopoetics. Drawing on their research of the last twenty years, they discuss intersections and differences of these two approaches within their respective geographical contexts, North American and European (and more specifically French). They locate ecocriticism and zoopoetics in the complex and plural histories of their emergence and development while also making comparisons with similar fields such as geopoetics and environmental humanities. They foreground key objectives such as decentering the human and grounding language in the body, advocating for subjects deemed “unsuitable,” bringing together literature, ecologies and animal space-time, examining their objects of study from the perspective of the text’s individual stylistic innovations, reconfiguring literary canons and literary histories, and inventing new narratives and explorations around terms such as oikos, machines, arche…) Examining notions like identity, limits and interstices, they underscore the political and ethical dimensions of ecocritical or zoopoetic literature. Finally, they affirm the ever-changing frontiers and constantly evolving perspectives of their two fields.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".