Deepening Understanding in Qualitative Inquiry
Bibliographic record
Abstract
In this paper the authors describe how the use of multiple methods of qualitative data collection over a two-year period, including interviews, concept maps and journals, and the analysis of data through visual inquiry, categorizing (constant comparison thematic analysis), and connecting (narrative analysis) provided a more comprehensive understanding of the process of evolution in college teachers’ perspectives on teaching and learning within a professional development program than would have emerged with only a single method . Concept maps provided an initial visual footprint of teachers’ emerging perspectives. Categorization revealed four major patterns across teachers’ perspectives. Connecting the data through narrative summaries exposed a contextualized rendition of aspects of individual teachers’ perspectives. Each of these three approaches offers a unique lens into qualitative data analysis, and when used together, they clarify important aspects of the phenomenon under investigation.
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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 | 0.260 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.013 | 0.095 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".