Bibliographic record
Abstract
Contemporary mainstream discussions of the Supreme Court are often qualified with the warning that the nine justices are out of touch with everyday American life, especially when it comes to the newest and most popular technologies. For instance, during oral argument for City of Ontario v. Quon, a 2010 case that dealt with sexting on government-issued devices, Chief Justice John Roberts famously asked what the difference was “between email and a pager, ” and Justice Antonin Scalia wondered if the “spicy little conversations ” held via text message could be printed and distributed. While these comments have garnered a great deal of attention on the internet, the Court has just as often addressed difficult constitutional questions regarding technology in a nuanced and informed manner. For this paper, I have selected six recent cases that deal with technology. Three of these cases concern technology and free speech, while the other three involve technology and the right to privacy. By reading oral argument and seeing how the justices discuss technology, I have attempted to define in each case how familiar the Court was with the specific innovation that was in question. Then, through this lens, I have analyzed the rulings to show where the Court’s technological nuance resulted in well-reasoned
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".