Individual Autonomy, Law, and Technology: Should Soft Determinism Guide Legal Analysis?
Bibliographic record
Abstract
How one thinks about the relationship between individual autonomy (sometimes referred to as individual willpower or human agency) and technology can influence the way legal thinkers develop policy at the intersection of law and technology. Perspectives that fall toward the `machines control us' end of the spectrum may support more interventionist legal policies while those who identify more closely with the `we are in charge of machines' position may refuse to interfere with technological developments. The concept of soft determinism charts a middle-ground between these two positions and could assist in the formulation of a general theory of the relationship between law and technology. Soft determinism maintains that technological developments are embedded in social, political, economic and other processes, and serve to guide and, potentially, configure future actions and relationships with these technologies, their users, and their subjects: while past technology develops shape the present, individuals and groups can still exert control over these technological developments.
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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 | 0.033 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.108 |
| Scholarly communication | 0.016 | 0.036 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.013 |
| 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".