Bibliographic record
Abstract
Robert F. Kennedy once said, “There is a Chinese curse which says, ‘May he live in interesting times.’ Like it or not we live in interesting times. They are times of danger and uncertainty; but they are also more open to the creative energy of men than any other time in history. And everyone here will ultimately be judged—will ultimately judge himself— on the effort he has contributed to building a new world society and the extent to which his ideals and goals have shaped that effort.” There is little doubt that the judges in the United States and Canada “live in interesting times.” We face three enormous challenges: First, there is the budget. From California to British Columbia, the courts have seen a serious erosion of court funding. What has happened there is devastating, but not being as bad off as those courts is hardly acceptable. In part, lack of funding explains the membership challenge the American Judges Association faces. Many courts no longer pay for memberships in organizations like the American Judges Association or the National Association for Court Management. Despite our claim to the mantra: Voice of the Judiciary®, AJA isn’t going to single-handedly fix court funding. AJA must offer a reason for judges to spend their own money to be an AJA member.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.427 | 0.401 |
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".