From Online Political Posting to <i>Mansplaining</i> : The Gender Gap and Social Media in Political Discussion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The gender dynamics of political discussion are important. These dynamics shape who shares their political views and how they share their views and reactions to these views. Using representative survey data from the United States and the UK, we investigate how social media platforms shape the gender dynamics of political posting. We find that on Facebook, gender does not predict political posting, whereas on Twitter, the gender gap is more pronounced. We also examine the concept of “mansplaining”—a term used to describe a patronizing form of communication directed at women by men. Firstly, we find that posting about political issues to Twitter is more likely to result in being an explainee but also being an explainer of political issues. Furthermore, posting to Twitter increases the likelihood of men reporting having been accused of mansplaining and women reporting having experienced it. In general, more than half of the women say they have experienced mansplaining, especially those who are younger, well educated, and left-leaning. We argue that the possibility of being mansplained affects who is willing to post their opinions online, and as such, caution should be exercised when using digital trace data to represent public opinion.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it