Calvinball: Users’ Rights, Public Choice Theory and Rules Mutable Games
Bibliographic record
Abstract
This article proposes the “rules mutable game” as a metaphor for understanding the operation of copyright reform. Using the game of Calvinball (created by artist Bill Watterson in his long-running comic strip Calvin & Hobbes) as an illustrative device, and drawing on public choice theory’s account of how political change is effected by privileged interests, the article explores how the notion of a game in which players can modify the rules of the game while it is being played accounts for how users are often disadvantaged in copyright reform processes. The game metaphor also introduces a normative metric of fairness into the heart of the assessment of the copyright reform process from the standpoint of the user. The notion of a rules mutable game tells us something important about the kinds of stories we should be telling about copyright and copyright reform. The narrative power of the “fair play” norm embedded in the concept of the game can facilitate rhetoric which does not just doom users to dwell on their political losses, but empowers them to strategize for future victories.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".