Mennocostal Musings: Poetic Inquiry and Performance in Narrative Research
Bibliographic record
Abstract
My narrative research investigates the writing of two critically-acclaimed Canadian Mennonite authors. My methods include interviews with the authors and narrative analysis of their works. I also use a less conventional method, that of writing poetry. Through writing poems about my "mennocostal" (Mennonite and Pentecostal) background, I am coming to new understandings of my self, my past experiences, and my writing-research practices. In turn, these insights help me better understand some experiences and writing practices of my research subjects, as well as what the scholarly literature says about such practices. I research how writing personal narratives can be an act of inquiry—how it can help the writer construct new understandings about her self and her topic. While studying how writing can be inquiry, I practice writing as inquiry. I also perform the poetic data from my research. In this article, I perform some poems through audio files (http://natashagwiebe.googlepages.com/poeticperformances) and give examples of how writing them is making me a better researcher. Along the way, I mention how participating in poetic performances as a listener and performer has helped shape my poetic inquiry and engendered new insights into my narrative research. I conclude by situating my poetic inquiry as performative research. URN: urn:nbn:de:0114-fqs0802423
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.039 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".