Ethical Challenges and Interpretive Difficulties with Non-Clinical Applications of Pediatric fMRI
Bibliographic record
Abstract
In this article, we critically examine some of the ethical challenges and interpretive difficulties with possible future non-clinical applications of pediatric fMRI with a particular focus on applications in the classroom and the courtroom - two domains in which children come directly in contact with the state. We begin with a general overview of anticipated clinical and non-clinical applications of pediatric fMRI. This is followed by a detailed analysis of a range of ethical challenges and interpretive difficulties that trouble the use of fMRI and are likely to be especially acute with non-clinical uses of the technology. We conclude that knowledge of these challenges and difficulties should influence policy decisions regarding the non-clinical uses of fMRI. Our aim is to encourage the development of future policies prescribing the responsible use of this neuroimaging technology as it develops both within and outside the clinical setting.
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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 | 0.166 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.017 |
| 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; 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".