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
Humans are experts in understanding social environments. What perceptual and cognitive processes enable such competent evaluation of social information? Here we show that environmental content is grouped into units of "social perception", which are formed automatically based on the attentional priority given to social information conveyed by eyes and faces. When asked to segment a clip showing a typical daily scenario, participants were remarkably consistent in identifying the boundaries of social events. Moreover, at those social event boundaries, participants' eye movements were reliably directed to actors' eyes and faces. Participants' indices of attention measured during the initial passive viewing, reflecting natural social behaviour, also showed a remarkable correspondence with overt social segmentation behaviour, reflecting the underlying perceptual organization. Together, these data show that dynamic information is automatically organized into meaningful social events on an ongoing basis, strongly suggesting that the natural comprehension of social content in daily life might fundamentally depend on this underlying grouping process.
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 | 0.000 |
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