Are We Equal Online?: An Investigation of Gendered Language Patterns and Message Engagement on Enterprise Communication Platforms
Bibliographic record
Abstract
It was previously hypothesized that gender differences -- and thus gender discrimination -- would disappear if communication was no longer in person, and instead was transmitted and received in the same format for all. Yet, even online, researchers have identified gendered language styles in written communication that reveal gender cues and can lead to unequal treatment. In this work, we revisit these past findings and ask whether the same gendered patterns can be found on modern communication platforms, which present a new set of engagement features and mixed synchronous capabilities. We quantitatively analyze 335,000 Slack messages sent by 845 individuals as part of 46 teams, collected over six years of a product design capstone course. We found little evidence of traditionally gendered communication styles (characterized as elaborate, uncertain, and supportive) from the minority-gender participants. We did identify relationships between message author gender, communication style, and message engagement --- women and minority genders were more likely to have their messages engaged with, but only when using certain communication styles --- suggesting complex power dynamics exist on these platforms. We contribute the first study of gendered language styles on Enterprise Communication Platforms, adding to the community's understanding of how new settings and emerging technology relate to team collaborative dynamics, and motivating future tool development to support collaboration in diverse teams.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".