Bibliographic record
Abstract
We have the pleasure of introducing this sixty-eighth volume of the American Journal of Comparative Law. This volume will mark the seventh year of our tenure, a time during which the Journal has become a—rewarding and demanding—part of our daily routine. The last year has been, again, an exciting year. We received many excellent submissions, and we are proud of the quality of the articles we were able to publish, and of the breadth of issues we covered in 2019. As we are finalizing our line-up for 2020, we hope to stay true to our goals of making accessible to our readers comparative law scholarship as diverse as possible, from all over the world, from as many theoretical and methodological perspectives as possible. As always, we wish to express our gratitude for the work done by the members by the Executive Editorial Board and, in particular, by our Book Review Editors, Professors Richard Albert, of the University of Texas at Austin, and Joshua Karton, of Queen’s University, Kingston, who just finished his first year as Book Review Editor. We also wish to thank our Articles Editor, Amber Lynch, of the McGill University Faculty of Law, who organizes our logistics with a steady hand despite many challenges. She deserves our profound gratitude for her wonderful work.
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 | 0.009 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.066 | 0.064 |
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".