Eight ways to get a grip on intercoder reliability using qualitative-based measures
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
The use of quantitative intercoder reliability measures in the analysis of qualitative research data has often generated acrimonious debates among researchers who view quantitative and qualitative research methodologies as incompatible due to their unique ontological and epistemological traditions. While these measures are invaluable in many contexts, critics point out that the use of such measures in qualitative analysis represents an attempt to import standards derived for positivist research. Guided by extant research and our experience in qualitative research, we argue that it is possible to develop a qualitative-based measure of intercoder reliability that is compatible with the interpretivist epistemological paradigm of qualitative research. We present eight qualitative research process-based guidelines for evaluating and reporting intercoder reliability in qualitative research and anticipate that these recommendations will particularly guide beginning researchers in the coding and analysis processes of qualitative data analysis.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 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