Challenging the Ivory Tower: Proposing anti-racist geographies within the academy
Bibliographic record
Abstract
The experiences of academic women of colour in geography have not been discussed in detail in our discipline. While we have witnessed an gradual increase in the literature about women of colour within feminist geography, transnationalism, and diaspora studies, among other subfields, we have yet to thoroughly explore how geography's historical engagement with colonialism and imperialism work to ensure the continued domination of whiteness among faculty and students within geography. This special issue brings to light the experiences of some women of colour who research, teach and work within the discipline of geography, suggesting some future avenues for more emancipatory geographies within the academy. Cuestionando la Torre de Marfil: Proponiendo geografías antirracistas en la academia Las experiencias de mujeres académicas del color en la geografía no se han discutido con todo detalle en nuestra disciplina. Mientras hemos presenciado un aumento en la literatura acerca de mujeres del color dentro de la geografía feminista, transnationalism, y los estudios de diáspora, entre otro subfields, nosotros tenemos mas examinar cómo compromiso histórico de geografía con el trabajo del colonialismo y el imperialismo para asegurar la dominación continuada de la blancura entre la facultad y estudiantes dentro de la geografía. Este asunto especial revela las experiencias de algunas mujeres del color que investiga, enseña y trabaja dentro de la disciplina de la geografía, sugiriendo algunas avenidas futuras para geografías más emancipadoras dentro de la academia.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| 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".