New Emerging Technologies in Qualitative Research
Bibliographic record
Abstract
According to Mayan (2009) being a qualitative researcher means to "enjoy living and learning with people to collectively make sense of our world. Qualitative research is not only done with people, it is also accomplished through people…" (p. 12). By virtue of its various definitions, qualitative research involves a great deal of human communication. Communication has a major role in all aspects of qualitative research from planning to execution. While many new qualitative research technologies have evolved over the past few decades, the most critical and influential ones are those related to communication technologies. As there is limited data about the use of communication technologies in qualitative research, the purpose of this paper is to provide an overview of the new emerging technologies in qualitative research. We provide descriptions of the evolving technologies and highlight the importance of qualitative researchers being up to date with these developments.
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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 | 0.443 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".