Collaborative Zoom Coding—A Novel Approach to Qualitative Analysis
Bibliographic record
Abstract
During the current coronavirus (COVID-19) pandemic, web conferencing became a staple in professional communication, with new and evolving applications amidst unique social distancing measures mandated across the globe. In this article, we describe Collaborative Zoom Coding (CZC) as an adaptive approach to qualitative data analysis that our research team developed in light of social distancing measures imposed due to the COVID-19 pandemic. CZC uses the web conferencing platform Zoom, to help analyze data. Our team used CZC to develop a code book for the community-based research (CBR) project, Sexual Health and Diasporic Experiences of Shadeism (SHADES). CZC enabled all team members to participate in data analysis by providing opportunities for group training and real-time collaborative data analysis, irrespective of team members’ location and level of experience with research. This article describes our specific processes for CZC and outlines its advantages as well as challenges. We conclude with a discussion of how researchers can conduct collaborative coding using Zoom and other conferencing technologies to further democratize the research process, particularly for community-based research endeavors.
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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 | 0.121 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".