Whose Geography, Whose History? Reimagining How We Teach the History of Geography
Bibliographic record
Abstract
In this roundtable, we share our reflections on how to teach more critical histories of geography in Anglo-American institutions. Ashutosh offers a contrapuntal reading of the history of geography, looking for ways to represent both the forces of consolidation and resistance in telling the history of our discipline. Last considers how we might “re-expand” the history of the discipline through the teaching of countergeographies. Sundberg argues that what is at stake in teaching the history of geography is whether or not we will reproduce an imperial way of life. Wilson, scholar of critical geographic information systems, reflects on geographic technologies in the history, present, and future of geographic thought. Lave considers the treatment of physical geography in the history of geographic thought and imagines a geography fit to the task of addressing the intersecting crises of capitalism and the climate. Looking beyond the history of geography to the composition and culture of our discipline, Craig, Noble-Varney, and Ehrkamp argue that how we read histories of geography is equally as important as what we read. Learning to read with a diversity of others—and welcoming discomfort—is key to reshaping the stories we tell about geography, in graduate seminars and in our broader engagements with the discipline.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.069 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".