Qualitative Research in the Digital Era: Obstacles and Opportunities
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.
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 | 0.553 | 0.464 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.028 | 0.052 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".