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
Perceptual body size distortions have traditionally been studied using subjective, qualitative measures that assess only one type of body representation-the conscious body image. Previous research on perceived body size has typically focused on measuring distortions of the entire body and has tended to overlook the face. Here, we present a novel psychophysical method for determining perceived body size that taps into implicit body representation. Using a two-alternative forced choice (2AFC), participants were sequentially shown two life-size images of their own face, viewed upright, upside down, or tilted 90°. In one interval, the width or length dimension was varied, while the other interval contained an undistorted image. Participants reported which image most closely matched their own face. An adaptive staircase adjusted the distorted image to hone in on the image that was equally likely to be judged as matching their perceived face as the accurate image. When viewed upright or upside down, face width was overestimated and length underestimated, whereas perception was accurate for the on-side views. These results provide the first psychophysically robust measurements of how accurately healthy participants perceive the size of their face, revealing distortions of the implicit body representation independent of the conscious body image.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it