Feeling otherwise: Ambivalent affects and the politics of critique in geography
Bibliographic record
Abstract
Scholars across the social sciences and humanities have increasingly questioned the meaning and purpose of critique. Contributing to those conversations, some geographers have advocated for affirmative or reparative practices such as reading for difference or experimentation that seek to provoke more joyful, hopeful, or enchanting affects, as alternatives to what they perceive as a prevailing forms of ‘negative’ critique. In response, others have re-emphasized the centrality of negativity and revalued negative affects in the context of regimes of racialization, heteronormativity, and coloniality. Rather than taking sides in a debate thus framed, this article develops an ambivalent position that foregrounds multiple senses of difference that exist within affirmative and reparative projects. Drawing on feminist and queer geographic work, the explicitly political and difference-oriented writing of Sedgwick and Deleuze, and queer and postcolonial affect scholars, we argue for critique characterized by an ambivalent and pluralistic attitude toward feeling. Joining those arguing for a pluralization of the moods and modes of critical work, our readings suggest the necessity of a pluralism that refuses any escape from the ‘negativity’ of the social field in favor of an affectively ambivalent engagement with the inherent politics of critique in a plural and uneven world.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.207 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".