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
Disgust can be thought of as an affective system that has evolved to detect signs of pathogens, parasite and toxins as well as to stimulate behaviors that reduce the risk of their acquisition. Disgust incorporates social cognitive mechanisms to regulate exposure to and, or anticipate and avoid exposure to pathogens and toxins. Social cognition entails the acquisition of social information about others (ie, social recognition) and from others (ie, social learning). This involves recognizing and assessing other individuals and the pathogen/parasite/contamination/toxin threat they pose and deciding about when and how to interact with and, or avoid them. Social cognition provides a frame-work for examining the expression of disgust and the associated neurobiological mechanisms. Here, we briefly consider the relations between social cognition and pathogen/parasite/toxin avoidance behaviors. We briefly discuss aspects of: (1) the odor mediated social recognition of actual and potentially infected individuals and the impact of parasite/pathogen threat on disgust mate and social partner choice; (2) the roles of "out-groups" (strangers, unfamiliar individuals) and "in-groups" (familiar individuals) in the expression of disgust and pathogen avoidance behaviors; (3) individual and social learning of disgust and empathy for disgust; (4) toxin elicited disgust and anticipatory disgust; (5) the neurobiological mechanisms, and in particular the roles of the nonapeptide, oxytocin and estrogenic mechanism associated with social cognition and the expression of disgust. These findings on the social neuroscience of disgust have a direct bearing on our understanding of the roles of disgust in shaping human and nonhuman social behavior.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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