Negotiating the insider–outsider dilemma in urban research: Experiences of a graduate student returning home for fieldwork
Bibliographic record
Abstract
Abstract African doctoral students studying abroad and returning to their home countries for fieldwork face multiple and complex challenges. This paper reflexively addresses the question of positionality from the experiences of conducting research on urban governance and the spatial politics of street traders in Harare, Zimbabwe. The paper discusses dilemmas associated with navigating insider and outsider identities, showcasing how these categories continually shift while conducting research on street traders within a distinct socio‐cultural and political context. Moreover, the author's background as a former street trader, now pursuing a PhD at the University of Western Ontario in Canada, adds a layer of complexity to the situation, offering valuable insights into how these ‘multiple’ positionalities can either facilitate or hinder data collection. The paper underscores the nuanced experiences of the researcher in the field, shedding light on the potential challenges, pitfalls and opportunities inherent in grappling with one's positionality. By foregrounding these complexities, the paper contributes to our understanding of the positionalities of researchers in the social sciences and adds to the growing body of literature on methodologies for conducting urban studies, particularly with vulnerable populations.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.039 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".