Bibliographic record
Abstract
This paper examines an environment where original content can be remixed by follow-on creators. The modelling innovation is to assume that original content creators and remixers can negotiate over the 'amount' of original content that is used by the follow-on creator in the shadow of various rights regimes. The following results are demonstrated. First, traditional copyright protection where the original content creators can block any use of their content provides more incentives for content creators and also more remixing than no copyright protection. This is because that regime incentivises original content creators to consider the value of remixing and permit it in negotiations. Second, fair use can improve on traditional copyright protection in some instances by mitigating potential hold-up of follow-on creators by original content providers. Finally, remix rights can significantly avoid the need for any negotiations over use by granting those rights to follow-on innovators in return for a set compensation regime. However, while these rights are sometimes optimal when the returns to remixing are relatively low, standard copyright protection can afford more opportunities to engage in remixing when remixing returns are relatively high.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".