A Review of Research on Coding Methods for Open-ended Text Responses in Survey Questionnaires
Bibliographic record
Abstract
In fields such as social sciences and market research, open-ended questions can collect richer data information, but how to effectively count and analyse these text answers becomes a key issue. The study mainly explores the three coding methods of open-ended questions in questionnaires, including the definition, process, and application of manual coding, semi-automatic coding, and automatic coding. According to existing literature and data, manual coding has high flexibility and accuracy, but it is inefficient when processing large-scale data; semi-automatic coding combines manual coding and machine learning technology, which can improve efficiency while maintaining a certain degree of accuracy; automatic coding relies on natural language processing technology and deep learning models, which greatly improve coding efficiency, but there is a problem of insufficient accuracy when facing complex semantics. Future research can focus on improving the accuracy of automatic coding through deep learning, developing intelligent semi-automatic systems that reduce manual intervention, and incorporating real-time feedback mechanisms for continuous misappropriation.
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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 | 0.151 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".