Is There a Definition? Ruminating on Poetic Inquiry, Strawberries and the Continued Growth of the Field
Bibliographic record
Abstract
Over the last ten years, Poetic Inquiry (PI) has proven itself as an emergent arts-based research methodology. It has gained greater acceptance in the larger community of qualitative research due in large part to the hundreds of published studies that employ the writing or analysis of poetry as a major focus of the research process (Finley, 2003; Prendergast, Leggo & Sameshima, 2009; Prendergast & Galvin, 2012). However, despite this greater acceptance and increase in studies found in the literature, there has not been a critical contemporary exploration of the history, theory and method of PI that could lend itself to defining what the method is, for those unfamiliar with it. This article provides a summary of PI as it exists in the literature today. This includes surveying the rhizomatic history of the method, exploring debates around who should or should not use the method and conversation around the current uses of PI in qualitative research.
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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 | 0.063 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.015 | 0.133 |
| Scholarly communication | 0.022 | 0.050 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".