Analyzing List-Style Open-Ended Questions: Combining Texts from Individual Answer Boxes Improves Classification with Language Models
Why this work is in the frame
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Bibliographic record
Abstract
Abstract List-style open-ended questions allow for multiple answers. Previous research on the design of such questions found that providing multiple small answer boxes yields more and richer answers than providing one larger answer box. Using a series of classifiers based on the Bidirectional Encoder Representations from Transformers language model, we empirically study how this design choice affects the classification of such answers. We design a 2 × 2 factorial experiment: (i) analysis with a multi-label versus single-label classifier and (ii) answers obtained from one larger answer box versus multiple smaller answer boxes. We find that the multi-label classifier gives more accurate results than the single-label classifier (1 percent versus 9 percent misclassification of individual labels), regardless of how the answers were obtained. Surprisingly, analysis with a multi-label classifier is preferable. We attribute this success to the classifier’s ability to use label correlations. We conclude that list-style open-ended questions should continue to provide multiple answer boxes due to better data quality. However, answer boxes should be concatenated for analysis to improve classification performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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