Review Essay: Guidance in the World of Computer-Assisted Qualitative Data Analysis Software (CAQDAS) Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This review discusses Christina SILVER and Ann LEWINS' book, "Using Software in Qualitative Research: A Step-by-Step Guide" (2nd ed.). This book is an impressive undertaking, with online supplemental material in the form of three data sets consisting of many different types of data, detailed instructions for seven CAQDAS (Computer-Assisted Qualitative Data Analysis Software) programs, and full-color reproductions of illustrations from the book. The 14 chapters in the book cover a wide range of analysis issues when working with software programs, and the authors encourage critical use of such tools. Readers will benefit from engaging with the online supplemental tools. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1502223
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.081 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it