Bibliographic record
Abstract
Much of the attention in team-based qualitative research has been on reflexivity, subjectivity, and emotionality in the relationships between researchers and subjects during data collection and analysis. There has been less emphasis on the relationships among researchers, especially the social dynamics of inter-coder agreement in what we call in this article ‘social reliability’. We explore three aspects of social reliability during team coding: explicit team knowledge, implicit team suppositions and assumptions, and explicit and implicit emotionality. Inter-coder reliability is not merely a methodological and scientific issue, but also a social one. Researchers ignore it at their peril. We suggest that researchers should endeavour to develop ways of explicitly recognizing and incorporating social reliability into their projects in order to enrich our understanding of research subjects.
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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 | 0.582 | 0.718 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.012 | 0.054 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".