Reflection/Commentary on a Past Article: “A Practical Iterative Framework for Qualitative Data Analysis”
Bibliographic record
Abstract
This submission is a reflection by Srivastava and Hopwood on their earlier article, A Practical Iterative Framework for Qualitative Data Analysis, originally published in International Journal of Qualitative Methods in 2009, and selected for the journal’s special anniversary issue, “Top 20 in 20.” They discuss how they have applied the framework in their various studies since then, Srivastava, primarily in field-based international research in education and global development, and Hopwood, in education and health. Based on a brief analysis of the paper’s citations, they identify its impact to have been: in a wide variety of fields crossing disciplinary boundaries, studies situated in a range of domestic and international contexts, studies analyzing data from intersectional perspectives and conducted with marginalized participant groups, referred to in methodological textbooks and publications, and used by researchers of all levels of experience, independently or in teams. They end by identifying what they consider to be key emerging topics associated with qualitative data analysis, Hopwood, on nonrepresentational and posthumanist perspectives and the implications of “postcoding,” and Srivastava on considering the agency of less privileged, marginalized, or vulnerable participants in data collection and analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.127 | 0.366 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.037 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.013 | 0.015 |
| Research integrity | 0.043 | 0.113 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".