Bibliographic record
Abstract
What can post-humanism teach us about game design? This paper questions the line drawn between what species and matter can play and what cannot play. Combining works by scholars of feminist post-humanism, new materialism, and game studies, primarily Jane Bennett, Donna Haraway, and T.L. Taylor, it proposes that play is a form of communication not only between animals and humans but also between plants and cyborgs, insects and atoms. Beginning by interrogating the borders of the human that have been built on ableist and racist discourses, this paper moves towards considering the human as interspecies and outlines that we must reassess the ways in which a multiplicity of species experience the intra-action that constitutes “play.” With a brief look into the history of defining play in both game studies and animal studies and their small crossover, play is reconfigured into an outlook or an approach rather than a set of rules. It is a drive that all species and matter experience, including insects, bacteria, and metal. This moves us beyond considering solely the materiality of our bodies at play by reconsidering the objects of play as our co-players, as matter with agential force. I argue that we need to reconsider the videogame player as an interspecies being, an assemblage of human and non-human bodies. The de-anthropocentricization of the popular notions of player agency allows for a multiplicity of reactions not created in the linear cause and effect course, the belief in ultimate player control within procedural systems, which dominates game studies. This paper concludes by submitting possibilities of what considering the non-human through a feminist and anti-ableist lens can offer game designers, players, and critics, such as considering the material platform’s impact on play, reforming the individualistic agency of players, and designing for the Other(s).
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".