User Engagement in the Context of Qualitative Data Analysis Software
Bibliographic record
Abstract
This research explores the qualitative researchers’ perceptions of computer assisted qualitative data analysis software (CAQDAS) through content analysis of blogs. The purpose of this exploratory work is to understand the existing relationships that scholars have with CAQDAS, and how the use of these tools promotes or hinders engagement during the research process.Au moyen d’une analyse de contenu de blogues, cette recherche explore les perceptions qu’ont les chercheurs qui emploient des techniques qualitatives à l’égard d’un logiciel (CAQDAS) d’analyse de données qualitatives assistée par ordinateur. L’objectif de cette phase exploratoire est de comprendre la relation existante entre les chercheurs et CAQDAS et comment l’utilisation de ce type d’outils engendre ou empêche l’engagement durant le processus de recherche.
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 | 0.246 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".