Using solicited audio-recorded diaries to explore the financial lives of low-income women in Kenya during COVID-19: perspectives, challenges, and lessons
Bibliographic record
Abstract
Solicited diaries in audio, written and online forms are increasingly used in qualitative data collection. However, most studies using this approach are set in high-income, high-literacy country settings. This paper discusses the opportunities and challenges of this approach in a low-income, low-resource, low-literacy setting. We used solicited audio-recorded diaries to explore the financial lives of low-income women in Kenya during the COVID-19 pandemic. We enrolled 24 women to submit diary entries every day for seven days. We found that the audio-recorded diaries worked well with low-income women in Kenya, which has high penetration of cell phone ownership. The diaries provided textured, detailed insights into participants’ day-to-day challenges, fluctuations, and coping strategies while relying less on recall. Nevertheless, the approach required two pilots to perfect, which may be challenging when research resources and time are limited. This study provides timely evidence on the use of audio-recorded solicited diaries in low-income settings.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".