Collaborative Zoom Coding—A Novel Approach to Qualitative Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it