Gendered Language in English-Medium Professional Digital Discourse: A Corpus-Based Study of Achievement Narratives and Interaction Dynamics on LinkedIn
Bibliographic record
Abstract
This study interrogates the persistence and evolution of gendered discourse within professional digital spaces, focusing on English-language interactions on LinkedIn as a site where achievement is publicly narrated and socially evaluated. Drawing on a balanced corpus of 50 achievement-oriented posts (25 male, 25 female) and 2,317 associated comments, the research integrates corpus linguistics and engagement analytics to identify how linguistic choices both reflect and reproduce gendered norms in professional self-presentation. Quantitative analysis comprising keyword frequency, keyness testing, collocational mapping, and dispersion measures was complemented by qualitative discourse interpretation to uncover the semantic and pragmatic framing of success. Engagement metrics, including reactions, shares, and comment sentiment, were examined using statistical tests with effect sizes to assess audience response patterns. Findings reveal that male-authored posts privilege competence, leadership, and strategic execution, while female-authored posts foreground ambition, collaboration, and gratitude often blending assertive positioning with relational framing. Although overall visibility did not differ significantly by gender, comment patterns diverged: women’s posts attracted proportionally more supportive and affective responses, whereas men’s elicited more strategic and analytical feedback. These patterns suggest that LinkedIn’s professional ethos mitigates but does not erase gendered communicative asymmetries. The study advances scholarship in sociolinguistics and professional discourse by offering empirical evidence from an underexamined platform, highlighting how subtle linguistic and interactional dynamics can shape perceptions of credibility, authority, and leadership potential in digital English professional networks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".