Buttressing regulation of cognitive enhancement devices with principles of harm reduction
Bibliographic record
Abstract
Maslen and colleagues offer an excellent model for regulating cognitive enhancement devices (CEDs), and we largely endorse their approach of extending medical device policy to include CEDs. Maslen et al. argue that since the risks and benefits of CEDs can be identified, consumers are best placed to evaluate the impact of these effects on their own wellbeing: 'experts are to assess what the risks are, the consumer how much they matter'. In principle, we agree: consumers should be allowed to decide what risks are worth taking, but the situation is somewhat more complicated, for the evidence that consumers are in a strong position to evaluate the many risks associated with CED use is lacking. Indeed, a glance at online forums on CEDs suggests that undue risks are already being taken. Importantly, given the ease with which devices can be built using easily obtainable parts, overly tough regulation will not effectively curtail use, but rather push it underground. For these reasons, we suggest that any regulatory framework be buttressed by principles of harm reduction, providing real-world users with expert-backed recommendations for safe use. We argue for the development of tools that facilitate this dialogue, while recognizing the challenges in so doing.
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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 | 0.068 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.082 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.040 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 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".