Reflections from Cross-Gender Fieldwork Experiences in Open Markets in Ghana
Bibliographic record
Abstract
Fieldwork can be an enjoyable academic adventure producing lifelong experiences of excitement and a sense of academic accomplishment. However, it can be an equally frustrating undertaking, especially when carried out in ‘unfamiliar’ environments. This paper adds to the growing number of studies about fieldworkers’ experiences by reflecting on the complexities involved in the process and proffering ways to respond to them. We share our perspectives as three adult African males conducting research in a predominantly female space in two informal markets in Accra, Ghana. To do this, we engage with five issues related to fieldwork: preparing to enter the field; negotiating access; handling interviews; dealing with ethical dilemmas; and exiting the field. We found that being male is not a barrier to conducting research in a predominantly female space. The success of our fieldwork was a product of our ability to adapt, be creative, appreciate our inadequacies, learn quickly and also take some practical and common-sense steps. Our hope is that the insights shared in this paper will serve as a compass for prospective fieldworkers.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".