Critical feminist reflexivity and the politics of whiteness in the ‘field’
Bibliographic record
Abstract
Feminist geographic commonsense suggests that power shapes knowledge production, prompting the long-standing reflexive turn. Yet, often such reflexivity fixes racial power and elides more nuanced operations of difference – moves feminist scholars have, in fact, long problematized. To counter this, we revisit Kobayashi's (1994) ‘Coloring the Field’ [‘Coloring the Field: Gender, “Race”, and the Politics of Fieldwork,’ Professional Geographer 46 (1): 73–90]. Twenty years on, and grounded in our fieldwork in South Sudan and Honduras, we highlight how colonial and gender ideologies are interwoven through emotion. Decentering a concern with guilt, we focus on the way whiteness may inspire awe while scholars of color evoke disdain among participants. Conversely, bodies associated with colonizing pasts or presents can prompt suspicion, an emotive reaction to whiteness not always fixed to white bodies. These feelings have significant repercussions for the authority, legitimacy, and access afforded to researchers. Our efforts thus disrupt notions that we, as researchers, always wield power over our participants. Instead we argue that the positioning of ‘subjects of color’ in the global south, racially and in their relationships with us, is historically produced and socioculturally and geographically contingent. Rethinking the field in this way, as a site of messy, affective, and contingent racialized power, demonstrates the insights offered by bringing together feminist postcolonial and emotional geographies.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.121 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".