Bibliographic record
Abstract
People make surprisingly accurate judgments about others based on minimal information. At zero-acquaintance, people are able to detect perceptually ambiguous information about others including their personality traits, general affect, and even the presence of a mental disorder (Daros, Ruocco, & Rule, 2016). This study examines whether people are able to detect how much someone’s mood varies from facial appearance. High-affect variability is associated with poorer psychological health along with poorer physical health (Jenkins, Hunter, Richardson, Conner, & Ressman, 2019; Hardy & Segerstrom, 2017). Given that high variability is associated with negative interpersonal consequences, it may be important to detect these qualities at first glance In Study 1, target participants (N = 200) will have their photograph taken, answer questions about their typical mood, and complete 5 daily diary surveys capturing how much their mood varies across time. In Study 2, perceiver participants (N = 80) will view targets’ facial photographs and indicate the extent to which they think targets’ mood fluctuates. It is predicted that perceivers will be able to accurately detect mood variability at zero-acquaintance. Future research can investigate the cues people use when making such judgments, and how this might influence future social decisions about who to befriend, date, or hire. Faculty Mentor: Miranda Giacomin Department: Psychology (Honours)
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.
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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".