mixSTM: Adapting the Structural Topic Model for a quantitative analysis of focus group data
Bibliographic record
Abstract
The Structural Topic Model (STM) incorporates external information about expected document-topic proportions to enhance the model. Motivated by focus groups, whose transcripts represent text data inherently grouped by session, we propose three extensions to the STM: 1) mean document-topic proportion estimation using a regression with random effects; 2) partitioned estimation of group-specific topic covariance matrices; and 3) a post hoc mixed effects regression on topic prevalence which incorporates latent variable uncertainty into the coefficient estimates. We explore the utility of these modifications through simulated examples and apply them to focus group transcripts from a pan-Canadian study on homelessness. The new methods, collectively the “mixSTM", improved topic model fit when there was complex group-related variation in topic prevalence and provided new avenues for interpretation. These methods may better represent analyst beliefs about qualities of grouped text data, although there is a risk of over-complicating the estimation given small, qualitative data sources.
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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 | 0.063 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".