Automatic Coding of Open-ended Questions into Multiple Classes: Whether and How to Use Double Coded Data
Bibliographic record
Abstract
Responses to open-ended questions in surveys are usually coded into pre-specified classes, manually or automatically using a statistical learning algorithm. Automatic coding of open-ended responses relies on a set of manually coded responses, based on which a statistical learning model is fitted. In this paper, we investigate whether and how double coding can help improve the automatic classification of open-ended responses. We evaluate four strategies for training the statistical algorithm on double coded data, using experiments on simulated and real data. We find that, when the data are already double-coded (i.e. double coding does not incur additional costs), double coding where an expert resolves intercoder disagreement leads to the greatest classification accuracy. However, when we have a fixed budget for manually coding, single coding is preferable if the coding error rate is anticipated to be less than about 35% to 45%.
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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 | 0.268 | 0.605 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".