“Exploring unchartered territories”: fieldwork experiences from researching street traders
Bibliographic record
Abstract
Purpose Doing qualitative research with vulnerable urban populations such as street traders present significant methodological challenges, which many researchers may not be prepared to handle. This paper aims to provide a reflective account of the authors' fieldwork experiences while conducting a study with street traders in Harare, Zimbabwe. Design/methodology/approach This paper draws data from a qualitative case study conducted with street traders in Harare's Central Business District (CBD). In this study, mixed qualitative methods were used including focus group discussions, semi-structured interviews and photovoice. Findings The study’s findings suggest that researching street traders is a complex process that requires flexibility, adaptability and creativity of researchers across the following aspects: gaining access in unfamiliar research contexts, building rapport and trust with participants, managing ethical dilemmas and addressing power imbalances between researchers and participants. Originality/value While there is a growing body of empirical research on street trading in the global south, there are limited studies that discusses the practical fieldwork experiences associated with conducting primary research with such vulnerable and dynamic urban populations. The authors highlight strategies and practical steps that can be taken to address these challenges. This paper emphasizes the need for flexibility and adaptability in researching street traders, as it is akin to exploring uncharted territories where conventional methodological templates may not be effective.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".