Sources of Difference in Reliability: Identifying Sources of Difference in Reliability in Content Analysis of Online Asynchronous Discussions.
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
This paper reports on a case study which identifies and illustrates sources of difference in agreement in relation to reliability in a context of quantitative content analysis of a transcript of an online asynchronous discussion (OAD). Transcripts of 10 students in a month-long online asynchronous discussion were coded by two coders using an instrument with two categories, five processes, and 19 indicators of Problem Formulation and Resolution (PFR). Sources of difference were identified in relation to: coders; tasks; and students. Reliability values were calculated at the levels of categories, processes, and indicators. At the most detailed level of coding on the basis of the indicator, findings revealed that the overall level of reliability between coders was .591 when measured with Cohen’s kappa. The difference between tasks at the same level ranged from .349 to .664, and the difference between participants ranged from .390 to .907. Implications for training and research are discussed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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