‘There are many Bangladeshis in New Delhi, but . . .’: methodological routines and fieldwork anxieties
Bibliographic record
Abstract
Abstract Thanks to scholars placed both within and outside geography, real efforts have been made of late to uncover and engage with the voices of marginalised groups. While population geographers have furnished a remarkable array of methodological devices to explore migrants' being and consciousness, feminist geographers have challenged the established conceptual framework of geographical field research by underscoring the significance of self‐reflexivity, positionality, and situatedness in the field. But as this account of fieldwork with undocumented Bangladeshi migrants in the marginal and ‘illegal’ spaces of slums in New Delhi demonstrates, these nouveau routines do not engage fully with the fieldwork anxieties experienced by the researcher. In particular, critical and self‐engagements alone do not allow the investigator to deal effectively with the complicated and often opaque landscapes in which fieldwork is conducted. The article introduces these highly fragmentary social spaces, suffused with power, but also charged with ambiguities and contradictions, questioning our understanding of undocumented migrant communities and their ties with other groups in the slums. The unelaborated ‘risks of everyday life’ negotiated by unauthorised immigrants necessitate a reworking of these broad routines in order to gain access to and conduct interviews with them. No substantive results from my study are reported in the paper; it should be read as a methodological contribution. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".