Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article reviews the development of the face-processing system from birth, during infancy and through childhood, until it becomes the sophisticated system observed in adults. We begin by discussing the following major theoretical issues concerning the development of face expertise: (1) nature versus nurture or the role of experience in face processing, (2) level of processing (i.e., global, basic, subordinate, individual) and expertise, and (3) type of processing (i.e., holistic, configural, featural). This general overview will be followed by a closer examination of individual studies that investigate the development of face processing. These studies will include a review of (1) development of differential processing of faces and objects, (2) development of differential processing of faces of different species, (3) developmental changes in processing facial identity, and (4) developmental changes in the categorization of faces. Our review of the developmental literature reveals early competence in face-processing abilities with infants presenting a preference for face stimuli and facial discrimination using featural, configural, and holistic cues. This early competence is then later refined as evidenced by age-related changes throughout childhood. Some of the refinements are likely due to the development of general cognitive abilities, whereas some others may be face-specific. WIREs Cogni Sci 2011 2 666-675 DOI: 10.1002/wcs.146 This article is categorized under: Psychology > Development and Aging.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it