Luddites No Longer: Adopting the Technology Tutorial at the Supreme Court*
Notice bibliographique
Résumé
I. IntroductionThe average Supreme Court Justice appointed to the Court at age fifty-three.1 Modern Justices remain at the Court significantly longer than their ancestors did, retiring at an average age just short of seventy-nine.2 A Justice appointed today will enjoy a potential tenure that fifty percent longer than that of their typical eighteenth- and nineteenth-century predecessors.3 To put it bluntly, the Court old, and it isn't getting any younger.4Many of the legal issues before the Court are much younger.5 Justice Elena Kagan, the youngest member of the current Court, has seen the rise (and fall) of the compact disc and the VCR, the evolution of video games from Pong to World of Warcraft, and the invention of both cell phones and the Internet. Technological progress challenges the Court by forcing it to adapt the law to fit new, often unique situations. impact of the new on substantive law really quite significant, Chief Justice John Roberts quipped in 2011.6This Note argues that the Supreme Court ill-equipped to meet the challenges presented by rapidly changing technologies. Part II chronicles some of the Court's recent technological troubles, and explains how the current system fails to bridge the Court's technological gap. Part III illuminates how the Court's often Luddite existence damages the law as well as the Court itself. Part IV proposes a solution: the Supreme Court should implement a form of the technology tutorial, a highly malleable process used in patent litigation to educate generalist judges about complex technologies. Through the use of tutorials, the Justices could enhance their understanding of the technologies underlying many difficult cases, resulting in more accurate, defensible, and responsible decisions while simultaneously boosting the Court's legitimacy. Part V briefly concludes.II. The Supreme Court's Technological TroublesThe Supreme Court has never been accused of being ahead of the technological curve. It was not until the mid-1990s that the Court's oral arguments could be heard outside the courtroom, and even then access was still extremely limited.7 Audio recordings of arguments were still zealously guarded into the early 2000s.8 The Court's first website launched in 2000,9 years after the popular growth of the World Wide Web.10 Carbon paper draftopinions circulated between the Justices through the 1960s.11The modern Court still clings to vestiges of the past. Chief Justice Roberts is known to write out his opinions in long hand with pen and paper instead of a computer.12 Justice Stephen Breyer recently confessed that he couldn't even understand the Oscar-winning film The Social Network, which chronicles the rise of social networking behemoth Facebook from creator Mark Zuckerberg's Harvard dorm room.13 In a similar vein, Justice Antonin Scalia explained to a congressional subcommittee, I don't even know what [Twitter] . . . . But, you know, my wife calls me 'Mr. Clueless.'14The Justices' technological ignorance often spills over into the courtroom, and even into the Court's written opinions. The following three cases-Reno v. ACLU,15 City of Ontario v. Quon,16 and Brown v. Entertainment Merchants Ass'n17-provide numerous examples of how a low-tech Court takes on high-tech cases, with troubling results.A. Case Examples1. Reno v. ACLU.-The 1997 case Reno v. ACLU was the Supreme Court's first hands-on encounter with the Internet.18 At issue in the case were First Amendment challenges to two provisions of the Communications Decency Act of 1996 (CDA), which prohibited the transmission of indecent material and the display of patently offensive messages to children.19 The fledgling Internet's capabilities and limitations were crucial to the Court's legal analysis,20 and the Court noted its reliance upon the extraordinary amount of fact-finding performed by the district court. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,007 |
| Communication savante | 0,012 | 0,012 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,010 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».