The Suzanne Mackenzie Memorial Lecture: Rethinking the politics of feminist knowledge production in Anglo‐American geography
Bibliographic record
Abstract
It was Suzanne Mackenzie who first introduced me to the radical potential of a feminist mode of knowledge production in geography. In this paper, in Suzanne's honour, I ask how well feminist work in Anglo‐American geography is faring in terms of still generating new possibilities of knowledge and of existence. In asking this question I explore the work feminist interventions have done, and not done, in advancing possibilities within the discipline. I examine three aspects of feminist work in geography: looking back to (the collective forgetting of) feminist work prior to the 1960s; looking around at (the limitations of) feminist approaches to methodologies and methods; and looking ahead, in this current era characterized by anxiety and precarity, to (the as yet not undertaken) work on addressing these issues in the academy. I conclude by discussing the future of knowledge production in feminist geography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".