Bibliographic record
Abstract
This Article seeks to begin a discussion on the development of legal analysis that broadly considers the interplay between law and technology. Part I provides background on the need for the development of this theory, which could draw from and inform traditional legal scholarship that studies discrete areas of technology law like intellectual property law. Part II scrutinizes cases and policy decisions within three non-traditional areas of technology law - contracts, tax, and privacy - to show how legal analysis in light of technological change can be broken down into two broad categories: (1) a 'liberal' approach that is more sensitive to the ways that technological change affects interests, while often seeking legal solutions that are less deferential to legal precedents and traditional doctrine; and (2) a 'conservative' approach that relies more on traditional doctrinal analysis and precedents. Part III elaborates on general principles of analysis that can be drawn from the liberal approach, which: (a) recognizes that the interplay between law and technology is complex and interactive; (b) requires flexible legal solutions when it is determined that technological developments are undermining interests (law is technology); (c) recognizes that the direct regulation of technology provides opportunities to indirectly regulate behaviour to promote optimal social policy (technology is law). In summary, the liberal approach scrutinizes whether, given current or anticipated technological settings, a legal rule will promote the attainment of policy objectives (is the legal rule scientific?). Part IV offers tentative observations on the ways that a law and technology theory can provide insight into the whole law by revealing that, for instance, during times of technological change the entire law adapts by becoming a more flexible and forward-looking system. A downside of this transformation is that the liberal approach destabilizes the law by undermining the usefulness of precedents, making it more difficult for lawyers to predict the outcome of cases for their clients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".