Confronting the Ugly Truth: The (Un)Making of a 'Good' White Teacher on the Canadian Prairies
Bibliographic record
Abstract
Through this autoethnographic inquiry into the writing of poststructural, critical race, and critical whiteness scholars, I sought understanding of the social and political forces that made me as a ‘good’ white female teacher on the Canadian prairies and the consequences of performing this subject role over four decades of teaching within the public education system. I visualized this inquiry as a puzzle whose interconnected pieces I was compelled to identify and understand as part of my exploration as to whether I could (un)make my constructed subject identity and performance as a ‘good’ white female teacher. I needed to understand both the role I filled so well, according to the expectations of the system I served, and the harmful consequences of that invested performativity so that I could explore possibilities for conscious identity (re)construction and performativity. My research findings point to significant and grave consequences for everyone involved, including Indigenous students, students of colour, white students, and me. My research also points the way to hope and agency on this personal and critically introspective journey of truth and reconciliation.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.049 | 0.048 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".