Truth and Decolonization: Filling the Educator Achievement Gap Darn It!
Bibliographic record
Abstract
Public attention on the systemic oppression of Indigenous people in Canadian Residential Schools raised by the Truth and Reconciliation Commission (TRC) marks a possible turning point for Canada to enter a respectful relationship with First Nations. Calls for widespread education initiatives that promote Indigenous perspectives indicate a path to reconciliation. However, access to this path is obstructed by two barriers: 1) recurring colonial approaches to enacting Indigenous education policies; and 2) teachers’ bewilderment upon facing such a steep learning curve. In an attempt to overcome these barriers, I designed a workshop to assist educators and I approached teachers to engage on a grassroots level. The workshop developed from a combination of critical policy analysis and autoethnography to provide an accessible overview of Canada’s legacy of colonialism. Delivery of the workshop revealed participants’ readiness to learn about colonization in Canada, while exposing the need for capacity building within the Ontario public education system for Indigenous leadership to direct TRC initiatives.
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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.033 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".