Video Games and Posthumanism: Another French Exception?
Bibliographic record
Abstract
For a while, two understandings of ‘posthumanism’ competed: on the one hand, posthumanism with its techno-anthropo-economic focus and its inspirations from hard sciences, technoscience studies and transhumanism; on the other hand, posthumanism, its with sociophilosophical focus and its (French) theoretical inspirations. However, scholars have become increasingly aware that posthumanism could only be a combination of these two aspects, to the extent that studies silencing one of them have rarefied... except in the Francophone – especially French – academia. Despite several attempted introductions (by Quebec scholars or French expatriates), French intellectuals and scholars indeed continue to produce a significant amount of writings that neglects the theoretical dimension of posthumanism (the decentring of the human subject, the undermining of dualisms) and convey paradoxically humanist (sometimes, reactionary) notions on the relationship between human and technology. Our paper will investigate whether French and Francophone videogames featuring posthumans echo the structural specificity of the French treatment of posthumanism, with questions such as: do French games question or reinforce humanist notions (mind-body dualism, anthropocentrism, human subjectivity and agency)? What is the relationship between the French intellectual landscape and its cultural productions, and do videogames bear any particularity in this matter? Are French games different from other Francophone productions?
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".