The Long Arm of the Law
Bibliographic record
Abstract
In this paper, we offer "something old, something new, something borrowed, and something blue." You decide which is which! This session heard from legal experts about topics, such as: the Supreme Court's decision at the end of March 2013 in the Kirtsaeng case; the various spillovers arising from the U.S. Department of Justice antitrust enforcement action against Apple and various e-book publishers for price fixing, including substantial settlement(s), subject to court approval; an intellectual property overview focused particularly on MOOCs; and a close look at some seemingly shifting (over time) court views about fair use, particularly transformative uses. All of these have been much in the news and as usual, we will have some of the most informed and library-savvy presenters on these topics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".