Of Geography and Race: Some Reflections on the Relative Involvement of the Discipline of Geography in the Spatiality of People of Colour in the United States
Bibliographic record
Abstract
This article examines the approach of the discipline of human geography in the United States to the theme of race and, by extension, its position toward people of colour. The article endeavours to reveal the relative implication of the discipline since its modern era in the reification of ideas and stereotypes that had traditionally been attached to people of colour and seeks to expose its partial role in the race-based spatial distribution of the population at large. Notwithstanding the typical change in geographic methodology in relation to race in the 1960s, this, however, was not accompanied by an adoption of a profound conception of race as a socio-historical construct that ought not to be gauged solely through the lens of quantification and empiricism. This concern has recently been echoed by a number of critical geographers who seem to be cognizant of the power and magnitude of race in the continuing spatialization of people of colour.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".