What Does Affect Theory Do? Or, How to Pay Attention to the Possibilities of Attending
Bibliographic record
Abstract
The present paper explores the role of affect theory in social and political critique, specifically in terms of how it relates to modes of attending in the context of theorizing. In this regard, I examine why affect theory has markedly reshaped the contours of social and political academic discourse in recent decades, and what alternatives to theorizing it introduces or enables new openings to. In order to answer these questions, I delve into the works of various scholars who use affect theory as a framework for theorizing. I posit that engaging in an affective mode of attending enables attention to structures of bifurcation rather than binaries, by conceptualizing theory in terms of beside-ness rather than beyond-ness. In doing so, I aim to shed light on what an affective mode of attending might be, and what affect theory can teach us about what it means to attend, or how to engage in alternative attendings. I conclude the paper with a consideration of the ‘So what?’ question—in other words, why is the attention to attending significant? By attending to the possibilities inherent in alternative attendings, affect theory illuminates that there need not be ‘outside-ness’ understood in the sense of ‘beyond-ness’ for there to be an out- side in the sense of an alternative. To attend to something from a different stance, which then conditions different contours for the possibilities enabled from that stance, means that there exist multiple ‘outsides’ from within the supposed ‘inside’.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.062 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".