Report on ReAnimate'24: 2024 Summer School on Retro Gaming History, Critic, and Development
Bibliographic record
Abstract
For many years, there has been an interest in ''old'' games, either real old games or recent games with an ''old'' look and feel. The retro gaming community has grown from very niche to mainstream, following the general gaming trend. Retro gaming has also entered the general psyche with books, movies, documentaries, articles, etc. becoming mainstream. However, despite this mainstream status and some recent books, retro gaming remains under-studied in academia and existing research rarely enters mass media. We proposed a summer school dedicated to retro gaming, which invited both the humanities and engineering fields to provide unique insights on retro gaming, both theoretical and practical, and opportunities for cross-fertilization among research fields. This summer school welcomed anyone interested in retro gaming. In particular, students in the humanities learned about general game development and the particularities of retro games while students in engineering learned about the history of gaming and theories about games and game design. This summer school, supported by the ACM SIGSOFT and Cloanto, featured lectures in the mornings and practical, hands-on sessions in the afternoon given by experts on (retro) games as well as site visits, panels, and discussions to foster exchanges, create a community, and promote the studies of retro games.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.317 | 0.113 |
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".