Bibliographic record
Abstract
We have the pleasure of introducing this sixty-sixth volume of the American Journal of Comparative Law. This volume marks the fifth year of our tenure as Editors-in-Chief, as well as the fifth year that the Journal is hosted jointly at the Georgetown University Law Center and the McGill University Faculty of Law. This new volume is also the second one to be published by Oxford University Press. This change has resolved a number of issues, but, typical of a time of transition, has also brought an array of new questions. As Editors-in-Chief, we are grateful to be able to draw on our new partner’s expertise in the complicated art of disseminating scholarly work in today’s world. We hope that as a consequence of the transition, we (or at least our successors) will eventually be able to focus predominantly on the substantive aspects of editorship and on the pleasures of reading the excellent scholarship submitted to us from all over the world. The Journal continues to be in the very fortunate position to distill its content, through a highly selective peer-review process, from a consistently high number of sophisticated submissions. With regard to the scholarship published in the Journal, we are thus looking back at an exciting year. Given the constant stream of submissions, a special issue around the work of Professor Pierre Legrand was published as a supplement, in addition to the four regular ones, to avoid slowing down the regular publication process. As in the past years, we have tried to remain faithful to the traditions that established the reputation of the Journal while also giving room to marginalized voices and new types of comparative scholarship. For 2018, we look forward to another volume that spans the entire bandwidth of the very best comparative law scholarship has to offer these days. As we start the new year, we wish to express our gratitude for the work done by the members of the Executive Editorial Board and by our Book Review Editors, Professors Richard Albert and Paul Dubinsky. In addition, our Articles Editor, Amber Lynch, of the McGill University Faculty of Law, deserves our profound gratitude for her wonderful work. A special thank you also to Claudia Hasbun, of the Georgetown University Law Center, who provided invaluable help for the 2017 special issue.
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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 | 0.009 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.102 | 0.091 |
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".