Qualitative Research in the Digital Era: Obstacles and Opportunities
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
Although the many sites and opportunities available to researchers through the development and proliferation of the Internet are well known, little attention has been paid to what digital technologies and the world's developing digital infrastructure can offer qualitative researchers for the actual process of doing research. This article discusses opportunities that now exist that we have experimented with and implemented in our own research, such as viral sampling strategies, wireless interviewing, and voice recognition transcription, as well as impediments we have encountered that stand in their way. Included in the latter are research ethics boards who often lack expertise in issues that arise in computer-assisted research, hardware/software costs and technological expertise for researchers, and university administrations who have not embraced infrastructure for qualitative research to the same extent they have supported quantitative research. The article closes with a look at the implications of emerging issues, such as the trend to cloud computing, the proliferation of mobile devices, and the maturation of voice recognition software.
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 | 0.168 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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