Bibliographic record
Abstract
In May 2005, Keith Aoki invited me to participate on a panel on "The Politics of Copyright Law" at the 2006 Association of American Law Schools ("A.A.L.S. ") mid-year meeting workshop on Intellectual Property in Vancouver, British Columbia. The panel, renamed "The Politics of Intellectual Property," and moderated by Keith, included talks by Justin Hughes, Mark Lemley, Jay Thomas, and me, and it was followed by three concurrent sessions on "The Politics Concerning Moral Rights," "The Politics of Global Intellectual Property, " and "The Politics of Patent Reform." I'm not sure what the organizing committee had in mind when it put together our panel. Judging from the speakers invited to participate, it seems likely that the organizers expected us to talk about how intellectual property ("IP") law plays out in Washington. (Mark and Jay had been active in extant efforts to draft patent reform legislation, Justin has served as a policy expert in the patent office, and I've spent a large chunk of my life writing about the copyright legislative process.) Since nobody gave us explicit instructions, though, I took the opportunity to talk about something that had been on my mind. Although the A.A.L.S. had recently begun to make Annual Meeting talks available as podcasts, it did not record the 2006 mid-year meeting, and the text of the talk I gave has been sitting unread on my hard drive ever since. A few months ago, Justin Hughes wrote to ask me for a citation to the talk. When I told him it had never been published, he suggested that I agree to publish it here
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".