Conducting Collaborative Qualitative Analysis Remotely
Bibliographic record
Abstract
There is a growing body of literature around digital research, specifically regarding data collection and how to pivot research designs to be more conducive to online and virtual research, but little in the way of how to analyze data remotely. In this article, we share firsthand experiences from a qualitative study utilizing Google apps, Zoom, and NVivo to organize data, establish coding protocols, document memos, develop codebooks, employ thematic analyses, and calculate intercoder reliability. We focus on practices and procedures that establish rigor and trustworthiness, facilitate researcher collaboration and collegiality, and increase gifted education researchers’ educational adaptability. Learning from our collective experiences conducting qualitative research remotely may be of interest to researchers and students with limited budgets and/or those who work remotely with collaborators within and across different institutions.
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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 | 0.123 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".