Model-driven coding with VPAT: The Verbal Protocol Analysis Tool
Bibliographic record
Abstract
We present the coding tool VPAT (Verbal Protocol Analysis Tool), specifically designed to facilitate model driven analysis of users' verbal data. Such model-driven coding presents challenges in terms of efficiency and reliability. Engineers and researchers need effective means of coding with complex, hierarchical, multi-level coding schemes. VPAT allows input of customizable coding schemes, quick and easy insertion of codes into text documents with a user-friendly interface, and extraction of codes for analysis purposes. VPAT was developed for research purposes with user-centered design methods with various stakeholders. The VPAT prototype runs in conjunction with MS Access and MS Excel. Comparative task analysis breakdowns of main coding tasks in VPAT and a leading coding application are presented to demonstrate advantages and tradeoffs. A case study of coding usability data for a healthcare information retrieval system is presented to demonstrate usage and benefits. VPAT is a specific tool suited to model-based coding, and was shown to increase speed and reliability.
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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 | 0.045 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".