How do institutions approach the use and preservation of videogames in their collections?
Bibliographic record
Abstract
Videogames are one of the most popular forms of entertainment both internationally and in the UK today. In recognition of this popularity, museums are treating videogames as culturally, socially and technologically significant objects that visitors can learn about and enjoy. Similarly, many universities are now offering courses in subjects such as game design and videogame studies and as part of this, offer videogame collections to use and borrow from their institutional libraries. In the US and Canada, many academic libraries already have more established videogame collections built over the past decade, but in the UK, there are very limited examples of university libraries offering similar services. Videogames also provide a challenge for conservators and others interested in preservation, with issues such as physical decay, bit rot and the complex copyright nature of videogames needing constant solutions. This project outlines a brief history of videogames and the current state of the videogame industry before going on to investigate how six different international institutions are approaching the use and preservation of their videogame collections. The institutions involved are The Centre for Computing History (UK), Living Computers: Museums + Labs, Videogames: Design/Play/Disrupt Exhibition (The Victoria & Albert Museum, UK), Fraser Library (Simon Fraser University, Canada), The Computer & Video Game Archive (University of Michigan Library, US) and Goldsmiths Library (Goldsmiths, University of London, UK). Interviews were completed with staff at these institutions and using coding, differences and similarities were identified in their approaches and discussed in detail, along with recommendations for areas of further research on this topic.
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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.019 | 0.024 |
| Scholarly communication | 0.043 | 0.026 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".