Reviews of Functional MRI: The Ethical Dimensions of Methodological Critique
Bibliographic record
Abstract
Neuroimaging studies involving human subjects raise a range of ethics issues. Many of these issues are heightened in the context of neuroimaging research involving persons with mental health disorders. There has been growing interest in these issues among legal scholars, philosophers, social scientists, and as well as neuroimagers over the last decade. Less clear, however, is the extent to which members of the neuroimaging community are engaged with these issues when they undertake their research and report results. In this study, we analyze the peer-reviewed review literature involving fMRI as applied to the study of mental health disorders. Our hypothesis is that, due to the critical orientation of reviews, and the vulnerability of mental health population, the penetrance of neuroethics will be higher in the review literature in this area than it is in the primary fMRI research literature more generally. We find that while authors of reviews do focus a great deal of attention on the methodological limitations of the studies they discussed, contrary to our hypothesis, they do not frame concerns in ethical terms despite their ethical significance. We argue that an ethics lens on such discussion would increase the knowledge-value of this scholarly work.
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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 | 0.587 | 0.831 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".