Intersecting Methodologies to Support the Telling of Stories in Education Research: Appreciative Inquiry Within Narrative Inquiry
Bibliographic record
Abstract
Narrative inquiry has often been merged with other methodologies to conduct research in schools. Its interweaving with appreciative inquiry as a methodology to research within education, however, is newly emerging. In this study, which interweaves the two methodologies, narrative inquiry and appreciative inquiry are used to examine stories of teaching and explore teacher identity—an evolution of narrative inquiry that facilitates the telling of participant school stories in a focused and intentional way through an appreciative inquiry framework. This paper explores the interweaving of the methodologies and provides an example of its use. It draws on a doctoral study titled Identity as pedagogy: Locating the shadows in the sacred space between, which examined the stories of teacher identities and the ways such stories manifest in pedagogy, with a group of teachers from a common educational jurisdiction in eastern Canada. The data that emerged through the appreciative inquiry process were narratively analyzed and understood through the common themes they presented.
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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 | 0.116 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".