The Alienation of Economic Rights and the Case for Stickier Copyright
Bibliographic record
Abstract
The limited empirical literature examining the effects of strengthened copyright laws suggests that the primary winners of copyright protectionism are the intermediaries, such as the publishers and distributors of creative works, rather than the authors themselves. This article argues that this is a result of the free alienability of authors’ economic rights: authors are divested of their copyrights at a significant discount in favour of the rights-aggregating intermediaries, leading to underpaid artists and an ultimate reduction in the quality of disseminated works, if not quantity. This article therefore proposes that copyright be made stickier by granting authors a right to terminate any exclusive grants of copyright after a term of 10 years. It looks at examples of similar rights from other jurisdictions and addresses a number of counterarguments from recent scholarship.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.059 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".