Qualitative Research without money: Experiences with a home-grown Qualitative Content Analysis tool
Bibliographic record
Abstract
Experience with young research students in South Africa, most of whom have few or no resources and are not supported by research infrastructure by their universities, shows that they have great difficulty in learning the techniques of qualitative research. Beginning as a simple idea, the development of an ad-hoc package intended to assist with the coding and categorisation of qualitative data led to a useful suite of facilities that contributed to at least four projects, one of which had the texts of 52 interviews to work with. It proved possible to import, structure and organise the research data in a way that then permitted useful export of charts, tables and text into papers and theses. With appropriate skills, researchers also found it possible to apply their own SQL queries to data that was now well structured and fully normalised (in terms of database design). Comparison with two commercial packages shows that many of the proclaimed features of the commercial packages were replicated, and in at least one instance they seem to have been exceeded.
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 | 0.075 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".