Poet, Teacher, Acadie: Using Poetic Inquiry as a Tool for Unearthing Identity
Bibliographic record
Abstract
Uncovering an authentic version of self-identity is at once difficult and profound. Our self-identity affects how we (re)present ourselves to the outside world and ultimately how we engage as educators. Through more than twenty years of writing experience, and a decade as an educator at the post-secondary level, I have learned that poetry has power. However, it is only recently that I have come to further appreciate its power to explore the links between place and self-identity (Vincent, 2020). Poetry offers a chance to dwell in the depths of self-identity while simultaneously tapping into the liminal spaces that often unconsciously frame who we are. This presentation focuses on my on-going journey of self-discovery as an Acadian Canadian, and explores how poetry, as a tool for textual analysis and self-analysis, has helped me to unearth previously unexamined parts of my identity. This presentation also includes demonstrations of ways that other educators, whether experienced in poetry or not, can use poetry and/or poetic techniques to begin to explore their own identities as educators.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".