Bibliographic record
Abstract
The 2015 final report of the Truth and Reconciliation Commission of Canada (2015a) states that educational institutions are part of the problem of systemic colonialism that persists across the country. Racism against Indigenous peoples is apparent across Canada, as in the United States, Australia, and elsewhere. In this context, we share our applied theoretical framework, the “default to deliberative mode of engagement framework,” or D2 framework, that we designed for ourselves as non-Indigenous, or settler, educators who contribute to decolonization processes by increasing students’ interest in traditional and contemporary Indigenous values, cultures, knowledges, and legal and governance processes. In this article we share our reflections on the value of the D2 framework as a guide that can assist users in decolonizing themselves. Moving away from what we call “default mode” of colonialism can be the toughest part of the decolonizing journey. Accompanying the D2 framework, we share narratives that illustrate the kind of daily actions that reflect deliberative civic engagement on the road to 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.050 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".