Posthuman Conjectures: Animal and Ecological Sciences in Marie Darrieussecq’s Dystopian Fiction
Bibliographic record
Abstract
Despite being published over twenty years apart, Marie Darrieussecq’s novels, Truismes (1996) and Notre vie dans les forêts (2017), share many features including their dystopian setting, urgent narrative tone, and themes of hybridity, corporeality and radical revelation. Deconstructing the boundaries between animal and human, nature and culture, human and machine, they invite the reader to move beyond anthropocentrism. In response to this invitation, I propose four posthuman conjectures, tracing the ethos of animal and ecological sciences in the two novels. First, I examine the ways in which the presence of non-human animal worlds requires imagining new subjectivities and writing embodied languages. Second, I move from the animal world to the machine cyborg who remains caught in the effects and affects of the techno-scientific complex in Darrieussecq’s dystopian fiction. Third, I consider the space made in both novels for death and dying as a non-metaphysical phenomenon situating humans in an eco-evolutionary web. Last, I define writing as a form of (post)human technology that the novels use to reject the notion of human superiority and to illustrate language’s capacity to imagine new, less-hierarchical paradigms.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".