A sinister subject: Quantifying handedness‐based recruitment biases in current neuroimaging research
Bibliographic record
Abstract
Approximately ten per cent of humans are left-handed or ambidextrous (adextral). It has been suggested that, despite their sizable representation at the whole-population level, this demographic is largely avoided by researchers within the neuroimaging community. To date, however, no formal effort has been made to quantify the extent to which adextrals are excluded from neuroimaging-based research. Here, we aimed to address this question in a review of over 1,000 recent articles published in high-impact, peer-reviewed, neuroimaging-focused journals. Specifically, we sought to ascertain whether, and the extent to which adextrals are underrepresented in neuroimaging study samples, and to delineate potential trends in this bias. Handedness data were available for over 30,000 research subjects; only around 3%-4% of these individuals were adextral-considerably less than the 10% benchmark one would expect if neuroimaging samples were truly representative of the general population. This observation was generally consistent across different areas of research, but was modulated by the demographic characteristics of neuroimaging participants. The epistemological and ethical implications of these findings are discussed.
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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 | 0.074 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".