Sex Differences in the Expression and Use of Computer-Mediated Affective Language
Bibliographic record
Abstract
Although women have been stereotyped as more emotionally expressive than men, the extant empirical evidence on sex differences in the expression and use of affective communication is equivocal. The authors examined the influence of sex and context on the expression and use of computer-mediated affective language in a sample of young adults. A total of 56 undergraduates (28 males, 28 females) were paired in same-sex dyads and randomly assigned to either a webcam or no webcam condition. The participants engaged in a 10-min free chat online conversation in the laboratory. Transcripts were objectively coded for the use of affective communication and traditional linguistic and conversational style measures. The analyses revealed separate significant Sex × Webcam Condition interactions on the affective quality of language used and the expression of computer-mediated emotion. Men in the webcam condition used significantly less active words than men in the no webcam condition and less than women in the webcam condition. Women in the webcam condition used significantly more emoticons than women in the no webcam condition or men in either condition. Men and women did not differ in their use of emoticons in the no webcam condition. Results suggest that sex differences in the use and expression of computer-mediated affective communication are context specific in an undergraduate sample. Findings are discussed in terms of their larger implications for understanding sex differences in the expression and use of emotion in face-to-face (FTF) social interactions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".