Bibliographic record
Abstract
Our teaching of narrative inquiry, shaped by a conceptualization of narrative inquiry grounded in a Deweyan theory of experience, works from a view of experience as embodied, always in motion, and shaped and reshaped by continuous interaction among personal, social, institutional and cultural environments. Given this experiential grounding, narrative inquiry is much more than telling or analyzing stories. Our focus is on learning to think narratively, that is, on learning to think with stories. Learning to think with stories highlights the relational, multiperspectival processes in which participants and narrative inquirers inquire into their lived and told stories attentive to the dimensions of temporality, sociality and place and with a focus on retelling and reliving lived and told stories in more thoughtful and responsive ways in the future. Through a series of storied moments, we show ways in which we intentionally create small responsive communities of sustained conversation enabling students to tell aspects of their lives through engaging in diverse narrative inquiry activities. We then illuminate the transformational power of response as lives meet within the three-dimensional narrative inquiry space and each teller is supported in retelling his/her stories in more attentive ways. As students learn to attend to their experiences in narrative ways each teller awakens to new ways of knowing and becoming a narrative inquirer.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".