Bibliographic record
Abstract
Scholars have observed that the adversarial system tends to provide courts with only a “small snapshot of the technological whole,” which in turn forms the record upon which broader legal pronouncements occur. As a result, they contend that legislatures should be more proactive in making rules governing complex and rapidly advancing technologies, and that courts must show deference to these rules. Other scholars retort that, in practice, legislatures often fail to update obviously flawed and outdated privacy provisions. Whether due to special interest influence, majoritarian dislike of criminal suspects, or other institutional constraints, legislative responses have been wanting. As such, courts often play a pivotal role in governing novel technologies. To help courts bear their burden more effectively, I make two general proposals. First, when courts must make or decide on the constitutionality of a rule, I suggest that the legislature should utilize the reference procedure, which is not inhibited by traditional trial constraints. Second, to aid courts in applying existing rules, I recommend tasking an independent institution with providing up-to-date reports of the current state of technologies expected to come before the courts. Counsel may use such complex and timely research to address gaps in technological evidence at trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.023 | 0.051 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".