Bibliographic record
Abstract
How do black aesthetics beckon us to transvaluate the narratively disciplined representation of care which assumes the possibility of non-pathological being? And why engage in aesthetic critique in the midst of unbearable overlapping catastrophes? In the context of black life, these perennial questions find renewed vigor in the wake of the COVID-19 pandemic as a large-scale interruption to the World’s designation of normality seizes upon blackness in order to establish a new societal equilibrium. This article elaborates upon Sylvia Wynter’s practice of decipherment in order to think through COVID-19 pandemic-era rap albums that can be read as placing force upon the wounded seams of social cohesion rather than stabilizing it. If Western onto-epistemic ordering employs narrative as an aesthetic ordering that violently crowds out the always already relationship between blackness and pathology, then black aesthetics have the ability to intervene into narrative, though “not [through] narrative means” (Sexton 2021, 16).
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.057 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".