Encountering Berlant part two: Cruel and other optimisms
Bibliographic record
Abstract
Abstract Part 2 of Encountering Berlant amplifies the promise of Lauren Berlant's influential concept of ‘cruel optimism’. Cruel optimism names a double‐bind in which attachment to an ‘object’ holds out the promise of sustaining/flourishing, whilst simultaneously harming. The lines between harming, sustaining, damaging and flourishing blur, sometimes collapsing entirely. By holding together opposites the concept exemplifies and performs the centrality of ambivalence to Berlant's thought, as well as their orientation to overdetermination and incoherence. Geographers and others have found in the concept a way of understanding the intersection between affective and political economies in the crisis‐present following the 2008 financial crisis. Together with Berlant's linked concepts such as ‘crisis ordinariness’ and ‘impasse’, cruel optimism has offered a way of understanding why detachment can be so difficult and how damaging conditions endure. Contributors begin from these starting points, amplifying the concept's promise: a new way of researching and writing about the reproduction of ordinary damage and harm. By writing from diverse encounters with Berlant's work, they move the concept in multiple directions, juxtaposing it with other optimisms across a variety of empirical scenes and locations. The result is a repository of what cruel optimism, and Berlant's mode of thinking‐feeling more broadly, offer geographers and others.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".