Women's International Thought in the Twentieth‐Century Anglo‐American Academy: Autobiographical Reflection, Oral History and Scholarly Habitus
Bibliographic record
Abstract
Abstract Methodologies of textual and linguistic analysis have long held sway in Anglo‐American practices of intellectual history. Such approaches tend to decouple the ideas being traced from the human subject, or scholar, producing the thought. Taking the lead from the rich theorising work done in feminist, gender, race and cultural histories, this article asks what changes in our understanding of intellectual histories of international thought when we connect the lived and bodily realities of the human subjects producing the ideas to the ideas themselves. In so doing, the article makes a case for the importance of fleshing out what the author calls ‘scholarly habitus’ and suggests the potential utility of oral history as a methodology for reconstructing ‘scholarly habitus’. The article will draw upon an oral history archive comprised of twenty interviews conducted with senior women International Relations scholars from the United States, Canada and the United Kingdom to flesh out this argument. The article argues that oral history, as a medium for autobiographical practice, can reveal aspects of how gender, race and class shaped the scholarly practice and career trajectories of these women, as well as shed light on the historical dynamics of the discipline of International Relations as a whole.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".