‘Mind Your Business and Leave My Rolls Alone’: A Case Study of Fat Black Women Runners’ Decolonial Resistance
Bibliographic record
Abstract
The Black female body has been vilified, surveilled, and viewed as ‘obese’ and irresponsible for centuries in Western societies. For just as long, some Black women have resisted their mischaracterizations. Instead they have embraced a ‘fat’ identity. But little research has demonstrated how Black fat women participate in sport. The purpose of this study is to show how Black fat women who run use social media to unapologetically celebrate Blackness and fatness. This research uses a case-study approach to illuminate a broader phenomenon of decolonial resistance through running. In addition to analysis of websites, blogs, and news articles devoted to Black women’s running, we discuss the (social) media content of two specific runners: Mirna Valerio and Latoya Shauntay Snell. We performed a critical discourse analysis on 14 media offerings from the two runners, including websites, Twitter pages, and blogs collected over a five-month period from September 2020–January 2021. The analysis examined how they represent themselves and their communities and how they comment on issues of anti-fat bias, neoliberal capitalism, ableist sexism, and white supremacy, some of the pillars of colonialism. Whereas running is often positioned as a weight-loss-focused and white-dominated colonial project, through their very presence and use of strategic communication to amplify their experiences and build community, these runners show how being a Black fat female athlete is an act of decolonial resistance. This study offers a unique sporting example of how fat women challenge obesity discourses and cultural invisibility and how Black athletes communicate anti-racist, decolonial principles.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".