Bibliographic record
Abstract
We find field evidence for what experimental studies have documented regarding the contexts and characteristics that make individuals more susceptible to priming. Just before U.S. Presidential elections, judges on the U.S. Courts of Appeals double the rate at which they dissent and vote along partisan lines. Increases are accentuated for judges with less experience and in ideologically polarized environments. During periods of national reconciliation—wartime, for example—judges suppress dissents, again, especially by judges with less experience and in ideologically polarized environments. We show the dissent rate increases gradually from 6% to nearly 12% in the quarter before an election and returns immediately to 6% after the election. That highly experienced professionals making common law precedent can be politically primed raises questions about the perceived impartiality of the judiciary. We cannot rule out the possibility that judges—who profess to be unbiased—are intentionally biased, which also raises the question of intentional bias of professionals who claim to be unbiased.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".