Navigating Sites for Narrative Inquiry
Bibliographic record
Abstract
Narrative inquiry is a methodology that frequently appeals to teachers and teacher educators. However, this appeal and sense of comfort has advantages and disadvantages. Some assume narrative inquiries will be easy to design, live out, and represent in storied formats in journals, dissertations, or books. For the authors, though, narrative inquiry is much more than the telling of stories. There are complexities surrounding all phases of a narrative inquiry and, in this article, the authors pay particular attention to thinking about the design of narrative inquiries that focus on teachers’ and teacher educators’ own practices. They outline three commonplaces and eight design elements for consideration in narrative inquiry. They illustrate these elements using recently completed narrative inquiries. In this way, the authors show the complex dimensions of narrative inquiry, a kind of inquiry that requires particular kinds of wakefulness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.022 | 0.035 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".