Confused, Frustrated, and Exhausted: Solving the U.S. Digital First Sale Doctrine Problem Through the International Lens
Bibliographic record
Abstract
Users worldwide enjoy digital goods such as music and e-books on a daily basis. They have become a major part of people’s lives, with uses ranging from lighthearted entertainment to serious educational pursuits. In many cases, convenience and affordability make digital goods more preferable than their analog counterparts. However, users often cannot use digital goods as freely as they would analog goods. Courts, legislation, and businesses prohibit those users, accustomed to reselling unwanted hard-copy books or vinyl records, from reselling digital books and music. This confuses users as to what they can actually do with their digital goods. This Note proposes that the United States adopts a digital first sale doctrine based on normative principles pulled from E.U. and Canadian copyright law.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.031 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| 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".