‘It Is Time to Operate Like a Woman’: A Corpus Based Study of Representation of Women in STEM Fields in Social Media
Bibliographic record
Abstract
This study explores the discourse of women in science, technology, engineering and mathematics or medicine (STEM) fields produced by Twitter users on social media, with a particular focus on language usage and function in this discourse. The exploration of the women in STEM discourse was achieved by collecting a body of tweets using popular hashtags addressing women in STEM from the last week of October 2017. Following a corpus-based approach, this study analyzes the most frequent evaluative adjectives and 4-grams. Results from the analysis of evaluative adjectives show that Twitter users represent women in STEM fields positively by using positive adjectives such as great, amazing, inspirational etc. Furthermore, the analysis of the most frequent 4-grams reveals that Twitter users employ hashtags such as #ilooklikeasurgeon and #womeninSTEM to promote the work of women in STEM fields, show their appreciation of women working and studying in STEM and challenge prevalent gender stereotypes of STEM professions. It was found that the production of women in STEM discourse by most Twitter users has contributed to increasing the strength of women in the STEM community in social media, evidenced by their practices of advocacy, networking and challenging gender biases online. The discourse of women in STEM in social media is an example of discursive activism that focuses on the larger dialogue of women in STEM and highlights dominant forms of sexism and gendered stereotypes of women’s work in male dominated professions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".