Generative AI and the information commons: controversy, copyright, and closure
Bibliographic record
Abstract
Knowledge or information commons is a critical concept in communication and information policy necessary to understand generative AI (genAI) governance. We introduce the concept of the commons as an existing problem for AI governance and develop a conceptual framework for commons management solutions for genAI. We then evaluate if commons management informs active AI governance. Focusing on efforts in the United States, we analyse regulatory submissions to the Office of Copyright, drafting a new policy for generative AI. The case selection follows an established link in the field between intellectual property and commons management. Collectively, through mixed computational and qualitative analysis, we argue that submissions demonstrate the importance of commons management to AI governance, but the insufficiency of commons management solutions for AI. We conclude by outlining how common theory must address the challenge of AI.
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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 | 0.035 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.085 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".