“Active Waiting”: Habits and the Practice of Conducting Qualitative Research
Bibliographic record
Abstract
Learning to conduct good qualitative research passes beyond the acquisition of research knowledge and technical skill. A variety of attributes and abilities are important in the research process such as creativity, flexibility, and inquisitiveness, among others. Quality in qualitative research also requires the development and practice of specific habits. Such habits are likely a taken-for-granted aspect of qualitative inquiry for seasoned researchers; they might not be as obvious for less experienced researchers or students. In this article the author examines the role of habits in and on the practice of qualitative research. To illustrate this topic, he examines how researcher habits can influence the pacing of an inquiry. Qualitative research requires the learned practice of active waiting: striking a balance throughout a research project between moving forward and advancing the research process and, on the other hand, allowing adequate time for the full development of each aspect of the research.
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 | 0.404 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.075 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".