Post-brelfie: the limits of intersubjectivity & intersectionality in spring 2020 virtual lactation selfie culture
Bibliographic record
Abstract
In 2015, brelfies, digital self-portraits taken by breastfeeding mothers, began to emerge on social media platforms. In the spring of 2020, two virtual lactation events emerged as new sites of brelfie culture: laczoom and the #dropemoutchallenge. I define these two events as evidence of a new wave in brelfie culture, what I call “post-brelfie” cultures. “post-brelfie” cultures are determined by two primary differentiating tenets from brelfie culture: 1) Post-brelfie events occur on video-based new media platforms, as opposed to through digital photography-based ones, which inhibit this trend’s potential for both self and community empowerment. 2) Post-brelfies are a product of their socio-temporal moment: the novel coronavirus pandemic and its publicly mandated stay-at-home orders. Through a hybrid methodology combining cross-platform analysis, grounded theory, and contextual visual discourse analysis, the findings of this study assert that not only has brelfie culture thus far failed to realize its feminist and public health goals, but also that such aims have been further devalued under pandemic circumstances in which digital inequities have further siloed online communities, leading to the transmission of negative affects in these networks. Such affects are further exasperated by postfeminism and neoliberalism and serve to undermine the aims of intersectional feminism.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".