The politics of #diversifyyourfeed in the context of Black Lives Matter
Bibliographic record
Abstract
In the past decade, the idiom “diversify your feed” (DYF) has emerged concurrently with the rise of social media and communicates the idea that “following” accounts presenting a range of bodies and identities online creates inclusive digital environments and enhances wellbeing. In May of 2020, the tragic death of George Floyd at the hands of the Minnesota police has led to a surge of momentum for the Black Lives Matter movement, which has been highly visible on social media as well as in public life. As online communities grapple with how best to engage with anti-racism via the digital, a number of strategies have taken hold as methods through which individuals can actively challenge racism in their own lives and in the lives of others. Among the various strategies advocated is the idea that social media users “diversify their feed” by following Black influencers, activists, businesses, and creatives. In this short essay, we move beyond prevailing understandings of DYF as a practice to improve body image, to critically examine the ethics associated with this social media practice as a method of engagement with anti-racism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.047 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".