Longing <i>as Method</i> : A Rant on Yearnings for Our World, Academia, and Utopian Futurities Beyond Liberalism(s)
Bibliographic record
Abstract
Together we found ourselves discussing current moments of racialized formations, pandemic practices, academic policing, equity projects (equity, diversity, and inclusion [EDI] initiatives), and our use, among it all. Like Adrienne Maree Brown, we were “formulating [our] critique of the ways that social justice movements have felt, and where [our] longing for something else was strongest” (2017, p. 44). This article, therefore, used longing as method for unearthing those embodied yearnings that arose within us against discontents, empty promises, and institutional lackings toward useful questionings, hope-full cravings, and progressive desires for more just futurities for our world(s), academies, and utopias beyond. In conversation with various radical thinkactors, this rant traces our longings as method for considering something otherwise. We begin with, and continually revisit, a question triggered by our readings of Ahmed: what is our use?
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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 | 0.102 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.122 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".