Advancing rigour in solicited diary research
Bibliographic record
Abstract
Solicited diaries/journals are increasingly popular as an innovative qualitative method in the social sciences for better understanding people’s everyday lived experiences. In this article we create a framework for maintaining rigour while using such diaries. First, we systematically evaluate 43 research papers focusing on the method, drawing on Baxter and Eyles’ (1997) seminal evaluation of rigour in qualitative human geography research. We ascertain that significant improvements could be made to procedures for obtaining and analysing diary content. Second, we develop a framework to encourage rigour in diary research. We test our framework by evaluating research conducted by two of our authors who employed solicited diaries with street vendors in Vietnam. We propose that our analysis and framework can help social scientists improve the rigour of solicited diaries as a research method, and provide a model for enhancing rigour in other emerging qualitative approaches.
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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 | 0.715 | 0.813 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".