Signed Networks for the US Supreme Court Overturning its Prior Decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper introduces the idea of studying the decision citation network of the US Supreme Court in a new fashion by focusing on this Court’s overturning of some of its prior decisions. Two departures from current practices were developed. One was to consider the phenomenon of overturning in a broader network context. The second was to treat the citations between overturning decisions and the overturned decisions as negative ties. This led to the creation of multiple signed citation networks. These networks were studied to get a better understanding of the operation of this Court. The results show that, frequently, when decisions are overturned, this is not done in a logically consistent fashion. A research agenda is proposed regarding a reexamination of stare decesis , thought to be a bedrock of the US legal system, and calling it into question as a genuine operating legal principle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it