The Autonomy of Technology: Do Courts Control Technology or Do They Just Legitimize Its Social Acceptance?
Bibliographic record
Abstract
This article draws on the suggestion that modern technology is “autonomous” in that our social control mechanisms are unable to control technology and instead merely adapt society to integrate new technologies. In this article, I suggest that common law judges tend systematically to support the integration of novel technologies into society. For example, courts sometimes require parties seeking compensation for serious injuries to submit to medical technologies to which the parties object for genuine reasons of fear or moral objection. Where a novel technology alters the environment in some way, courts sometimes legitimize that alteration by refusing to recognize harm and instead characterizing avoidance of the technology as self-imposed harm. The examples selected in this article were chosen to support the hypothesis in one way or another, and future work will aim to look for counter examples and to conduct a more complete assessment of the hypothesis.
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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 | 0.025 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.065 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".