Bibliographic record
Abstract
I confess that I’m not at all certain why I was invited to this distinguished gathering. I suppose that it’s because Schlegel and I are friends; but that just pushes the mystery back a remove. Because as Jack surely recognizes, it is not at all obvious that (or why) we should be friends. We are from different generations—Schlegel is my senior by nearly twenty years. I don’t imagine that anyone would suggest that our personal styles bear a strong resemblance to one another. And our approaches to scholarship are almost antagonistic. Jack’s message accompanying the invitation to this conference strongly discouraged contributions focused on legal doctrine. For better or worse, that is the topic about which I have been thinking and writing for much of the past three decades. When I first introduced Schlegel to my wife at the Toronto meeting of the American Society for Legal History in 1999, he smiled broadly, pointed at me, and told her, “He does the best of the kind of work that I hate.” The closest he has ever come to complimenting my approach is to say, “I don’t believe in your internalism, but I understand it.” But the tone of reproach was unmistakable. As he said on another occasion, writing “doctrinal history” was a “misplaced” use of my energies. (In fairness, I must concede that he actually has said nicer things, but I don’t want to damage his cred by quoting them 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.652 | 0.411 |
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".