Bibliographic record
Abstract
This paper explores multiple approaches to building an art game project created from a feminist perspective. Funded by a research grant, this can be seen as an experimental praxis that plays with connecting metaphors invoked in feminist theory to playable media. This connection is figurative not literal and manifests throughout the development process: in conception (artistic intent), production (technical approach) and engagement with existent and emergent theory. Intentionally playing in the space between art games and game art and inspired by Haraway’s Cyborg Manifesto, PsXXYborg1 is an art game in development that presents a rich cyber-feminist mythos across multiple screens as an allegorical play with the eternal fascination of 'becoming-machine'. PsXXYborg blends feminist art practice, makerism and academic research in order to birth itself as a glitch for the hermetically sealed structures of game culture. When politically motivated the game glitch aims at disturbing the hegemonic structures of normative game culture questioning the evident exclusions growing over time. Questions include: How can digital play represent and reflect the human condition? What is a feminist game? Why does society position play as inconsequential? How might we play our way to an equitable future?
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 | 0.008 | 0.006 |
| 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 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".