Analyzing List-Style Open-Ended Questions: Combining Texts from Individual Answer Boxes Improves Classification with Language Models
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".