Bibliographic record
Abstract
This article uses a case study of the multiple arcade machine emulator (MAME) to insist that emulation is an important aspect of digital game culture that should not be dismissed due to copyright concerns. The author argues that emulators should be understood as ludic technologies produced by hacking practices that helped spawn and continue to permeate video game culture. Furthermore, while it may be tempting to describe the MAME as a “counter archive” that challenges institutional models of preservation, by drawing on the work of Coleman the author insists the project is better understood as a hacking practice committed to reordering “technologies and infrastructures” (p. 515). From this perspective, instead of rejecting institutional archival perspectives that view documents as truth-telling entities, the project hacks the traditional notion of the archive by treating platforms as contingent entities and game code as authentic artifacts.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".