The Audacity of Affect: Gender, Race, and History in Linguistic Accounts of Legitimacy and Belonging
Bibliographic record
Abstract
This review considers research on language and affect, with particular attention to gender, that has appeared in the past two decades in ways informed by the recent effloresence of work on affect in feminist, queer, (post)colonial, and critical race studies. The review is selective: It focuses on a few key ways that recent research is responding to gaps identified in earlier research and opening up promising areas for future research. This review thus attempts to connect linguistic anthropological and discourse analytic studies more fully with contemporary debates in feminist, queer, antiracist, and postcolonial studies. In general, I look at the rise of more fully historical approaches; in particular, I look at (a) affect in imperial and other global encounters; (b) language, neoliberalism, and affective labor; and (c) terror and hate, compassion, and conviviality in public speech. It also considers why we are, at this particular moment, witnessing such interest in affect.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".