Social Justice Theories as the Basis for Public Policy on Psychopharmacological Cognitive Enhancement
Bibliographic record
Abstract
Psychopharmacological cognitive enhancements could lead to a higher quality of life of healthy individuals with lower cognitive capacities, but the current regulatory framework does not seem to enable access to this group. This article discusses why Sen’s Capability Approach could open up such access, while two other modern social justice theories – utilitarianism and Rawls’ Justice as Fairness – could not. In short, the utilitarian approach is proven to be inadequate, due to practical reasons and having a low chance of real-world success. Rawls’ Justice as Fairness seems to be problematic because of conflicting stances that follow from his First Principle of Justice. The Capability Approach has the greatest chance of success in the context of these substances, because of arguments that can be identified under the banners of agency/self-respect and the way the public views those who take the capability path out of their poor situation. The article also discusses general and practical problems with psychopharmacological cognitive enhancement that should be addressed when writing new policy on this topic.
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 | 0.057 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.039 | 0.030 |
| Insufficient payload (model declined to judge) | 0.008 | 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".