A Model-Assisted Approach for Finding Coding Errors in Manual Coding of Open-Ended Questions
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
Abstract Text answers to open-ended questions are typically manually coded into one of several codes. Usually, a random subset of text answers is double-coded to assess intercoder reliability, but most of the data remain single-coded. Any disagreement between the two coders points to an error by one of the coders. When the budget allows double coding additional text answers, we propose employing statistical learning models to predict which single-coded answers have a high risk of a coding error. Specifically, we train a model on the double-coded random subset and predict the probability that the single-coded codes are correct. Then, text answers with the highest risk are double-coded to verify. In experiments with three data sets, we found that this method identifies two to three times as many coding errors in the additional text answers as compared to random guessing, on average. We conclude that this method is preferred if the budget permits additional double-coding. When there are a lot of intercoder disagreements, the benefit can be substantial.
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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 | 0.053 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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