Bridging cultural divides with the power of poetry: an educator’s reflection
Bibliographic record
Abstract
In this article I discuss the ways in which writing poetry and reflecting on its meanings may be a valuable tool for promoting an educator’s reflexivity surrounding issues of reconciliation. As Canada embarks on the work of healing the difficulties its colonial past has caused its original inhabitants (i.e. Indigenous peoples), educators must explore ways in which they can contribute to a more socially just, democratic, and healthy society. By utilising the theoretical framework of Dialogical Self Theory (DST) to describe and explore identity and the writing of poetry as an exploration of self, it becomes possible for myself, as an educator, to unearth my own biases and begin to create safe spaces for identity exploration, learning, and healing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.042 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.009 | 0.028 |
| 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".