Coding Text Answers to Open-ended Questions: Human Coders and Statistical Learning Algorithms Make Similar Mistakes
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
Text answers to open-ended questions are often manually coded into one of several predefined categories or classes. More recently, researchers have begun to employ statistical models to automatically classify such text responses. It is unclear whether such automated coders and human coders find the same type of observations difficult to code or whether humans and models might be able to compensate for each other’s weaknesses. We analyze correlations between estimated error probabilities of human and automated coders and find: 1) Statistical models have higher error rates than human coders 2) Automated coders (models) and human coders tend to make similar coding mistakes. Specifically, the correlation between the estimated coding error of a statistical model and that of a human is comparable to that of two humans. 3) Two very different statistical models give highly correlated estimated coding errors. Therefore, a) the choice of statistical model does not matter, and b) having a second automated coder would be redundant.
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.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.050 | 0.008 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.006 | 0.002 |
| 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