A kiss is worth a thousand words: the development and validation of a scale measuring motives for romantic kissing
Bibliographic record
Abstract
Research reveals that romantic kissing plays an integral role in relational scripts depicting the progression of intimacy and the expression of love in romantic relationships. Despite its importance, romantic kissing is rarely the primary focus of research. Thus, the three studies reported here advance the literature by developing and validating a scale measuring romantic kissing motives (the YKiss? Scale) and examining gender differences in these motives. In Study One, an exploratory factor analysis yielded a two factor solution: the Sexual/Relational Motives Subscale, which included items related to arousal, love, attraction, and relational scripts; and the Goal Attainment/Insecurity Motives Subscale, which consisted of items pertaining to attaining resources, using kissing as a means, and boosting one's self-esteem. Participants reported kissing for sexual/relational motives more frequently than goal attainment/insecurity motives. In Study Two, a confirmatory factor analysis confirmed the two factor solution resulting in a 42-item scale that demonstrated excellent psychometric properties, with men reporting goal attainment/insecurity motives more frequently than did women. In Study Three, the YKiss? Scale demonstrated adequate temporal consistency. This research provides investigators and practitioners with a useful tool and important insights when measuring the multitude of reasons why adults engage in romantic kissing.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".