5. Exploring Identities Through Poetic Inquiry: Heartful Journeys Into Tangled Places of Complicated Truths and Desires
Bibliographic record
Abstract
One of the challenges we face in higher education is knowing who we are as individuals and as communities. Poetic inquiry (Prendergast, Leggo, & Sameshima, 2009) is a way into that knowing, a way of exploring our own identities and our relationships with each other. Poetic inquiry creates a space for evocative knowing. This research project, supported by the Acadia University Research Fund, included two graduate students as co-participants, one graduate student as co-investigator, and a principal investigator. Through writing, feedback, editing, and rewriting, we sought to create poetry that would show our identities as individuals and in relationships with our communities. We met for four three-hour sessions to write poetry, after reading the work of a poet / scholar. For our fifth session, we performed our poetry at a public reading that was advertised throughout the University community. Audience members were given a copy of our chapbook of poetry (Guiney Yallop, Naylor, Sharif, & Taylor, 2009), which included participant-selected pieces from our own work completed during, or between, the sessions.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".