Prompt-based and Fine-tuned GPT Models for Context-Dependent and -Independent Deductive Coding in Social Annotation
Bibliographic record
Abstract
GPT has demonstrated impressive capabilities in executing various natural language processing (NLP) and reasoning tasks, showcasing its potential for deductive coding in social annotations. This research explored the effectiveness of prompt engineering and fine-tuning approaches of GPT for deductive coding of context-dependent and context-independent dimensions. Coding context-dependent dimensions (i.e., Theorizing, Integration, Reflection) requires a contextualized understanding that connects the target comment with reading materials and previous comments, whereas coding context-independent dimensions (i.e., Appraisal, Questioning, Social, Curiosity, Surprise) relies more on the comment itself. Utilizing strategies such as prompt decomposition, multi-prompt learning, and a codebook-centered approach, we found that prompt engineering can achieve fair to substantial agreement with expert-labeled data across various coding dimensions. These results affirm GPT's potential for effective application in real-world coding tasks. Compared to context-independent coding, context-dependent dimensions had lower agreement with expert-labeled data. To enhance accuracy, GPT models were fine-tuned using 102 pieces of expert-labeled data, with an additional 102 cases used for validation. The fine-tuned models demonstrated substantial agreement with ground truth in context-independent dimensions and elevated the inter-rater reliability of context-dependent categories to moderate levels. This approach represents a promising path for significantly reducing human labor and time, especially with large unstructured datasets, without sacrificing the accuracy and reliability of deductive coding tasks in social annotation. The study marks a step toward optimizing and streamlining coding processes in social annotation. Our findings suggest the promise of using GPT to analyze qualitative data and provide detailed, immediate feedback for students to elicit deepening inquiries.
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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 | 0.014 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".