On Intersectionality: Decolonization, Inclusion, and Diversity-focused Pedagogies
Bibliographic record
Abstract
The focus of this Special Issue (English) is to divert attention from the insidious global discourses centered on the re-assertion of white dominance through anti-immigration policies, and shift the conversation to one centered on the important work being done in graduate educational research that acknowledge our history and reflects on our role(s) in the continued treatment of Indigenous peoples and people of colour within Canada. This is with the aim to engage in anti-oppressive pedagogies that not only envisage reconciliation, but that consider action towards reconciliation. This is evidenced through a discourse analysis of the First Nation, Métis, and Inuit Education Policy Framework in Ontario (Currie-Patterson & Watson), a quanti-qualitative study on racism and reverse racism with teachers in Alberta (Lorenz), an exploration of the historical and contemporary impacts of racism on children of colour in our schools (Brady), and the examination of the Truth and Reconciliation Commission of Canada’s First Report and the consideration of a context-responsive pedagogical framework for an education for reconciliation (Siemens).
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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.096 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.004 | 0.042 |
| Research integrity | 0.004 | 0.009 |
| 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".