Challenges to masculinity in a feminized digital space
Bibliographic record
Abstract
Purpose This study aims to explore the perceptions and reactions of men, who participate in a female-dominated online consumption space. It looked at the process of men, (re)negotiating their digital gendered identity on Pinterest. Design/methodology/approach A grounded theory-light approach was taken. Data were collected through 21 one-on-one semi-structured interviews with male Pinterest users. Subsequently, data were extensively coded and analyzed for the key themes and patterns. Findings Three core categories emerged, which speak to the ways men account for their practices on Pinterest as autonomous online agents. These categories were: awareness of Pinterest as a feminized digital space; limited sociality due to the solitary use of Pinterest (the exception being when collaborating with an intimate partner); and performed identities (k = 4) serving to justify the men’s activities on a female-dominated social networking site. Research limitations/implications The findings establish a firm theoretical basis for understanding male Pinterest users as autonomous online agents. However, reflective of this relatively small, exploratory qualitative project, the process-based interview questions did not render, particularly, long or rich narratives. Future qualitative research might endeavor to ask deeper, more open-ended questions. Originality/value This is an original study of men’s use of Pinterest. Research on the identity projects of men entering fields traditionally occupied by women and coded as feminine is established, there is a lack of understanding of how gender identity is (re)constructed digitally, especially on social media.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".