A Team-based Approach to Open Coding: Considerations for Creating Intercoder Consensus
Bibliographic record
Abstract
In this article, we discuss methodological opportunities related to using a team-based approach for iterative-inductive analysis of qualitative data involving detailed open coding of semistructured interviews and focus groups. Iterative-inductive methods generate rich thematic analyses useful in sociology, anthropology, public health, and many other applied fields. A team-based approach to analyzing qualitative data increases confidence in dependability and trustworthiness, facilitates analysis of large data sets, and supports collaborative and participatory research by including diverse stakeholders in the analytic process. However, it can be difficult to reach consensus when coding with multiple coders. We report on one approach for creating consensus when open coding within an iterative-inductive analytical strategy. The strategy described may be used in a variety of settings to foster efficient and credible analysis of larger qualitative data sets, particularly useful in applied research settings where rapid results are often required.
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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 | 0.703 | 0.788 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.024 | 0.034 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.014 | 0.040 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".