Bibliographic record
Abstract
This article is designed primarily for two groups of people: lawyers or trial judges who are wondering whether to become appellate judges, and people who have recently become appellate judges.But observers of appeal courts may also enjoy a peek behind the curtain.I write this after twenty-seven years as a justice on three Canadian Courts of Appeal. 1 I have done three studies about how appellate courts and judges do and should operate in the United States and Canada.Two studies were for the Canadian Judicial Council.Some very able and very busy American federal and state appeal courts gave me an intimate view of themselves hard at work; I also have had some part in training *Justice of Appeal, recently retired, Court of Appeal of Alberta, of the Northwest Territories, and the Territory of Nunavit.1. Canada has basically a fused court system: Most superior courts are both federal and provincial.Their judges are all federally appointed (and have tenure to age seventyfive).There are almost no intermediate Courts of Appeal in Canada.2. And a long time ago, I had some experience in teaching and writing on time management.I have written for years on civil procedure, and chaired Alberta's Rules of Court Committee.3. The appeal process will continue to involve reams of paper until all appeals are fully electronic.For news of recent developments in this connection, the reader might consult Philip G. Espinosa, The Paperless Court of Appeals Comes of Age, 15 J. APP.PRAC.& PROCESS 99 (2014) (describing the technologies adopted by an intermediate appeal court in the state of Arizona as it has moved toward paperless procedures).
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.201 | 0.190 |
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".