Faculty Opinions recommendation of Identifying Hallmark Symptoms of Developmental Prosopagnosia for Non-Experts.
Bibliographic record
Abstract
Developmental prosopagnosia (DP) is characterised by a severe and relatively selective deficit in face recognition, in the absence of neurological injury.Because public and professional awareness of DP is low, many adults and children are not identified for formal testing.This may partly result from the lack of appropriate screening tools that can be used by non-experts in either professional or personal settings.To address this issue, the current study sought to (a) explore when DP can first be detected in oneself and another, and (b) identify a list of the condition's everyday behavioural manifestations.Questionnaires and interviews were administered to large samples of adult DPs, their unaffected significant others, and parents of children with the condition; and data were analysed using inductive content analysis.It was found that DPs have limited insight into their difficulties, with most only achieving realisation in adulthood.Nevertheless, the DPs' reflections on their childhood experiences, together with the parental responses, revealed specific indicators that can potentially be used to spot the condition in early childhood.These everyday hallmark symptoms may aid the detection of individuals who would benefit from objective testing, in oneself (in adults) or another person (for both adults and children).
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.266 | 0.211 |
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".