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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".