Black lives matter and say her name: how intersectional solidarity strengthens movements for social justice
Bibliographic record
Abstract
What happens when social movements try to address internal inequalities that define marginalized identity groups? Do internal efforts to redress the marginalization of particular subgroups within activist ranks undermine broader relations of solidarity? While some commentators believe that recognizing differences can lead to fragmentation within social movements, ultimately weakening them; advocates for intersectional solidarity believe that it strengthens movements. We address this debate by analyzing retweet networks on Twitter (now X) over three discrete time periods to examine the impact of the #SayHerName campaign, which called attention to the intersectional marginalization of Black women, on the #BlackLivesMatter movement’s online discourse. Our analysis is based on the perspective that discourse on public social media platforms constitutes a crucial aspect of contemporary social movements like Black Lives Matter. We find that the introduction of the #SayHerName campaign increased network density among users of the hashtag #BlackLivesMatter, and that even after #SayHerName declined, the #BlackLivesMatter network was denser and more connected that it had been prior to the initiation of #SayHerName. We conclude that in this case, there is no indication that separate organizing around intersectionally marginalized identities fragments or weakens social movements’ online networks.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".