Summarized attribute preferences have unique antecedents and consequences.
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
) as well as their inference about how much they liked the attribute in the abstract (their summarized attribute preference). Our results suggest that summarized attribute preferences-despite being (weakly) grounded in functional attribute preferences-were affected by incidental aspects of the context in which people learn about them (i.e., the overall likeability of the pool of faces). Furthermore, we observed a double dissociation in the predictive validity of summarized and functional attribute preferences: Whereas summarized attribute preferences predicted situation selection at a distance (e.g., whether to join a new dating website based on a description of it), functional attribute preferences predicted situation selection with experience (e.g., whether to join a new dating website after sampling it). We discuss theoretical and methodological implications for the interdisciplinary science of human evaluation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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