Bibliographic record
Abstract
We have the pleasure of introducing this sixty-seventh volume of the American Journal of Comparative Law. The publication of the last issue of the previous volume (LXVI) marked the end of our first five-year tenure as Editors-in-Chief. Little did we know, when we started five years ago, how much the Journal would become a part of our daily routines, and how time-intensive, but also how rewarding, this work would be. During the last annual meeting of the American Society of Comparative Law, its membership voted to renew our tenure for another term. We gladly accept this mandate and are encouraged by this vote of confidence. The last year has been, again, an exciting year. On the occasion of the Congress of the International Academy of Comparative Law in Fukuoka, a supplement issue containing the American reports, edited by Vivian Curran and Frank Gevurtz, was produced and made freely accessible online. The Winter Issue featured a symposium on “Legal History and Comparative Law: A Dialogue in Times of the Transnationalization of Law and Legal Scholarship,” consisting of five contributions guest-curated by Thomas Duve, Director of the Max Planck Institute for European Legal History. We believe we have reason to be proud of the line-up of articles and the breadth of different methodologies that we published in 2018. As we start the new year, we wish to express our gratitude for the work done by the members by the Executive Editorial Board and by our Book Review Editors, Professors Richard Albert and Paul Dubinsky. A special thank you goes to Professor Dubinsky, Associate Professor and Director of Graduate Studies at Wayne State University Law School, who has resigned after many years spent with the AJCL. We wish him well in his future endeavors. At the same time, we welcome and are grateful to Joshua Karton, of Queen’s University, who has accepted to join as Book Review Editor. Finally, our Articles Editor, Amber Lynch, of the McGill University Faculty of Law, deserves our profound gratitude for her wonderful work. A special thank you goes to her also for the work she did on the Supplement Issue for the reports of the Fukuoka Congress. Our gratitude also goes to Stephen Janick, J.D. Candidate 2019, Georgetown Law, who helped with the copyediting of that issue.
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 | 0.008 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.135 | 0.122 |
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".