Intellectual Journeys towards Emotions: A Conversation among Feminist Scholars
Bibliographic record
Abstract
Abstract This Viewpoint contribution considers the recent turn to emotion and affect in the humanities and the social sciences. We present here a conversation between four scholars of gender, Ute Frevert, Chitra Joshi, Lynn M. Thomas and Valerie Walkerdine, reflecting on their personal intellectual trajectories. In their discussion, they examine how, when and why they began to explore the analytical potential of feelings and emotions in their scholarly work. The turn to emotions was motivated by a search for more nuanced perspectives on social hierarchies, and subjectivity and agency. Through their respective expertise on western Europe, India, East and Southern Africa and Britain, the four scholars address the global connections between studies of gender and the increasing import of emotions as an analytical lens.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.059 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".