Bishop’s University, Quebec Visual Methodology in Classroom Inquiry: Enhancing Complementary Qualitative
Bibliographic record
Abstract
This article presents the argument that combining visual methods with other qualitative research methods enhances the inherent strengths of each methodology and allows new understandings to emerge. These would otherwise remain hidden if only one method were used in isolation. In a qualitative inquiry of an elementary teacher’s constructivist literacy practices, categorizing and contextualizing strategies using grounded theory and narrative analysis were juxtaposed with visual analysis. The interactive, interpretive process of moving back and forth between visual and textual data demonstrated the power of visual images to explicate the complexities of classroom practice. Cet article affirme que le fait de combiner des méthodes visuelles avec d’autres méthodes qualitatives de recherche augmente les forces propres à chaque méthodologie et stimule l’émergence de nouvelles connaissances qui demeureraient cachées si une seule méthode était employée en situation isolée. Lors d’une enquête qualitative des pratiques constructivistes en littératie d’une enseignante à l’élémentaire, nous avons juxtaposé des stratégies de classement par catégories et de contextualisation basées sur une théorie à base empirique et une analyse narrative d’une part et une analyse visuelle d’autre part. Le
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".