"I Ran From It And Still Was In It": Meditations On Melancholy And Race...In The Meantime
Bibliographic record
Abstract
This project engages with the question of black liberation. My project asks two key questions: in the absence of liberation, what can or does exist? And will liberation ever arrive? I answered these questions by tracing the figured opposition of Afro-pessimism and Black Optimism. I argue that while many see these approaches or experimental analytics in an oppositional way, it is important to focus on the interregnum of these two critical dispositions if we want to understand the possibilities for a world otherwise. Remaining in the interregnum can allow us to trace how and in what ways the presuppositions of Euro-American constructions of modernity implode. In modernity, Blackness has always been a site of untimely meditation manifesting itself in different and inventive ways. I argue that our current frameworks informed and shaped by white supremacy limit our imagining a future without Blackness, without whiteness and without race, because modern ego formation relies on these very enslaving structures. Beyond just imagining, toward making real, my project asks: what do we do in ‘the meantime’ as we invent (a new now/present and therefore future)? What is rendered central in the meantime, this site of transition and suspension, is a not a linear movement. Rather, the meantime as a method and a device allows a reading of these two radical dispositions about Blackness that discloses the indissoluble relationship between the ontological nothing and Blackness as its sociopolitical allotrope in the logics of melancholia as the liminal end of the world. Instead, staying with and in “the meantime”, I show how collapsing this imagined opposition between Afro-pessimism and Black Optimism (as responses to the anti-blackness and violence against the slave) challenges the melancholic structuration of antiblackness and its contingent utilitarian concepts such as the ego that comes in the desire for a mother and homeland, as expressed through the ‘return’ to Africa for Black people, and the need and desire for a Black other, as expressed through the figure of the slave, for white people and white supremacy. I thereby arrive at a conversation that nuances race, melancholy and notions of liberation and conclude with reminders of the importance of love to and for revolution
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".