Copy Protection in Jet Set Willy: developing methodology for retrogame archaeology
Bibliographic record
Abstract
Video games, and more generally computer games, are unquestionably technological artefacts that have cultural significance. Old computer games in particular had to function under technical constraints that would be alien to many modern programmers, while at the same time providing something novel and at first foreign to consumers. How did their creators accomplish their technical feats, and what impact did that have for the player-consumer? The study of 'retro' computer games' implementation is one topic within the nascent area of archaeogaming.\n\nDigital rights management (DRM) continues to be a major issue in the protection and distribution of content in electronic form. In this article, we study an early example of the implementation of copy protection in the 1984 game Jet Set Willy, something that comprises both physical and digital artefacts. It acts as a vehicle to illustrate a number of methods that we used to understand game implementation, culminating in a full reconstruction of the technique. The methods we cover include: 'traditional' research, along with its limitations in this context; code and data analysis; hypothesis testing; reconstruction. Through this positivist experimental approach, our results are both independently verifiable and repeatable. We also approach the complex context of early DRM, its hacks and workarounds by the player community, and what precipitated the design choices made for this particular game.
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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 | 0.036 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".