A Seat at the Table and a Room of Their Own: Interconnected processes of social media use at the intersection of gender and occupation
Bibliographic record
Abstract
Social media have enabled people to connect with others in unprecedented ways. Existing scholarship has so far provided conflicting insights regarding what people do with these connections. Here I propose that to make sense of what people accomplish with social media-enabled connections, one needs to examine more closely their foundations. Specifically, one key way to understand social media-enabled connections is to consider how social media enable people to come together on the basis of joint social identities. This study focuses on how people use social media in ways that connect them to one another at the intersection of gender and occupational identities, i.e. two social identities that have been central to many organization studies and are critical in today’s societies. The study relies upon the qualitative investigation of how women and gender non-binaries data scientists used social media. The study reveals that, at the intersection of gender and occupation, people use social media to engage in three interconnected processes of promoting inclusion, co-producing equalizing resources, and fostering exclusive enclaves. It brings light to new ambivalence reflected in people’s uses of social media as they seek, simultaneously, to reshape gender dynamics in their occupation and to protect their reputation as competent workers. It unpacks why and how, with social media, the professional and the political have become intertwined.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".