Experience Leads the Way: What Makes Narrative Inquiry Critical
Bibliographic record
Abstract
Abstract This chapter addresses a sensitive topic in the field of education: the relationship between and among narrative inquiry, critical analysis, and critical theory. It argues that narrative inquirers are critical – but not in the same way that critical theorists are critical, although they may draw on the same literature and terms. To make our point, we unpack three of our peer-reviewed articles and highlight our theoretical frames and research moves to demonstrate criticality in narrative inquiry. We specifically discuss (1) titles and topics, (2) research frameworks, (3) historical and contemporary data, (4) use of participants' voices (words and feelings), (5) themes, and (6) new knowledge. We mostly argue that narrative inquiry exists because of experience. From experience, everything else unfolds – including criticality – depending on where the researcher in relationship with research participants, takes the inquiry. This chapter explicitly addresses a lived issue known both inside the narrative inquiry community and outside of it.
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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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.087 |
| Scholarly communication | 0.032 | 0.049 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| 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".