Bibliographic record
Abstract
This essay deals with queer theory and how it applies to the videogame Cloudpunk and the comic series Motor Crush. Both of these texts use cyberpunk settings to tell stories about finding hope in community. Each text features protagonists trying to navigate worlds where legal success is highly competitive and practically impossible. They must therefore turn to community building, mutual aid, and criminal activity to find happiness. This analysis views the texts through the lens of queer time and queer space making practices as outlined by J Jack Halberstam and Jose Esteban Muñoz. Central to this article’s exploration of these texts is the characters inability and/or refusal to fit neatly into the worlds they inhabit, and how they must therefore find success outside of accepted channels. Success is only found by these characters through an attitude that can be summarised by the queer anarchist meme “be gay, do crime”, which connotes a sense of mischief, solidarity, and standing up to authority. The rigid social hierarchies that are built into the dystopian worlds is reflected in the infrastructure of the cities, which do not account for anyone living outside of an accepted norm. It is therefore considered an act of radical solidarity to break the rules if it is in support of others.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".