#ButNotMaternity: Analysing Instagram posts of reproductive politics under pandemic crisis
Bibliographic record
Abstract
In this article, we perform a thematic analysis of a sample of 70 #ButNotMaternity Instagram posts. #ButNotMaternity is a hashtag that emerged in the United Kingdom during the COVID-19 pandemic whereby the public, healthcare workers and campaigners shared experiences and concerns about pandemic maternity care restrictions and their disproportionate disadvantages for pregnant women. In the article, we analyse four themes that emerged from our thematic analysis – Individual experiences, loneliness and overcoming adversity, Voicing anger and absurdity, Mobilising anger and calls to action and Coordinated activism. Thinking about #ButNotMaternity in the context of ‘freelance feminism’, our article has a twofold aim. First, we explore the concept of ‘freelance feminism’ through #ButNotMaternity, asking to what extent this campaign draws from freelance tactics. Second, we use the hashtag to illuminate maternity inequality and modes of resistance during the COVID-19 pandemic. Through our thematic analysis, we argue that while ‘freelance feminism’ might be becoming hegemonic as a dominant mode of organising feminist activism and resistance, inspired by Malik et al. (2020), we also showcase how creative campaigns are potential places where collective action, structural critique and resistance may emerge.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".