In, out and unspeakably about: taking social geography beyond an Anglo-American positionality
Bibliographic record
Abstract
There appears to be something of an anxiety-producing impasse in British social geography with repeated calls to examine its positionality. Simultaneously, British, indeed Anglo-American, social geography appears to be enjoying something of a renaissance in the new millennium. I argue that such a paradoxical situation owes its existence to the hegemonic narrative of Anglo-American social geography. Starting with an overview of the development of constructions of the social in British social geography, I explore the extent to which such recurring identity crises are opening up avenues for change. Turning to the place of Anglo-American social geography in the international field, I examine the specificity of the institutional and linguistic positioning of British social geography claiming that change will remain surficial until it develops ways of thinking that do not deny the multivocal voices that have made it what it is. This denial constitutes social geography's ‘unspeakable’ and has two intertwining dimensions, an unwillingness to engage with its own whiteness and to move outside its own established repertoire to encompass non-western knowledges. Speculating about the redirection of Anglo-American social geography, I make a claim for a social geography that is constantly reinventing itself in ways that desire difference.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".