Bibliographic record
Abstract
We have heard throughout the day of the enormous amount of rule of law work going on all over the world.This primarily began in the early nineties after the fall of the Berlin Wall and the collapse of the Soviet Union.The American Bar Association has been a leader in this work, but the work has also been carried out by dozens, indeed, scores of other organizations: national bar associations of countries throughout the world, NGOs, and several for-profit companies.'The ABA alone receives over $30 million a year in grants to carry out this work, and so we know that hundreds of millions of dollars have been expended in these programs over the past eighteen years. 2 From their beginnings, rule of law programs have benefited from the involvement of prominent national and international jurists and bar leaders.In the United States, several Justices of our Supreme Court have been leaders and outspoken advocates
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".