Bibliographic record
Abstract
This article uses a no reply racist email I received in 2023 as its point of departure for thinking through the importance of The Racial Contract in theorizing about contemporary ‘post-race’ racism’s cultural life because of the prevalent discourse that Black people can be racist too. It looks at the continuing presence of willful white ignorance on anti-Black racism and the tenacity of global white supremacy’s impact on lines of sight societally and in the academy using understandings from Charles Mills’ (1997) The Racial Contract and his 2007 chapter ‘White ignorance’ on white supremacy, whiteness, racialization, bodies, and epistemologies of ignorance. The discussion of the email reflects some of the impact of Mills’ ideas on my own work within Racism Studies and Cultural Studies. As someone from Stuart Hall’s Black British Cultural Studies tradition who draws on Mills’ work in my meditations on anti-Black racism and white supremacy, I illustrate the generative nature of his work within these (sub)disciplines by also including my own work on institutional anti-Black racism’s affects as part and parcel of the workings of the Racial Contract. This locates racial affective economies within the Contract itself as the glue that binds white ignorance and white supremacy on issues of race and racism even in the face of its refutation.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".