Understanding Computer-Assisted Qualitative Data Analysis Software as a Tool to Enhance Systematic Literature Reviews in Human Resource Development
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
Using literature reviews to identify new research avenues and provide novel theoretical insights is increasing, with the Systematic Literature Review (SLR) recently gaining greater attention from human resource development scholarship. Analyzing and making sense of literature can be insightful, but also daunting as it involves organizing and analyzing vast amounts of articles and data. Computer-Aided/Assisted Qualitative Data Analysis (CAQDAS) software can be used to support this process by organizing the literature to enable more fine-grained analysis, support analytical coding, explore patterns in the literature, and check for coding consistency. In this instructor’s corner we explain and illustrate some of the CAQDAS analysis actions that can support researchers with their SLRs.
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 | 0.037 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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