I think I like you: Spontaneous and deliberate evaluations of potential romantic partners in an online dating context
Bibliographic record
Abstract
Abstract The present research examined processes of impression formation within an online dating context. Across two studies, female participants formed impressions of a potential partner based on an online dating profile containing information about the target's facial attractiveness and self‐described ambition. Afterwards, deliberate evaluations of the target were assessed with a self‐report measure and spontaneous evaluations were measured with an affective priming task. The results showed that deliberate evaluations varied as a function of both self‐described ambition and facial attractiveness. In contrast, spontaneous evaluations varied only as a function of facial attractiveness. Experiment 2 further showed that these effects were independent of the order in which the two types of information had been encoded. The results are discussed in terms of associative and propositional processes, and the conditions under which these processes can lead to conflicting evaluations of the same potential romantic partner. Copyright © 2009 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.002 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".