Bibliographic record
Abstract
During the past few years considerable debate has arisen within academic journals with respect to the use of smart drugs or cognitive enhancement pharmaceuticals. The following paper seeks to examine the foundations of this cognitive enhancement debate using the example of methylphenidate use among college students. The argument taken is that much of the enhancement debate rests upon inflated assumptions about the ability of such drugs to enhance and over-estimations of either the size of the current market for such drugs or the rise in popularity as drugs for enhancing cognitive abilities. This article provides an overview of the empirical evidence that methylphenidate has the ability to significantly improve cognitive abilities in healthy individuals, and examines whether the presumed uptake of the drug is either as socially significant as implied or growing to the extent that it requires urgent regulatory attention. In addition, it reviews the evidence of side-effects for the use of methylphenidate which may be an influential factor in whether an individual decides to use such drugs. The primary conclusions are that neither drug efficacy, nor the benefit-to-risk balance, nor indicators of current or growing demand provide sufficient evidence that methylphenidate is a suitable example of a cognitive enhancer with mass appeal. In light of these empirically based conclusions, the article discusses why methylphenidate might have become seen as a smart drug or cognitive enhancer.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".