Bibliographic record
Abstract
This text is concerned with authentic race discussions in education spaces as an antithetical approach to disingenuous, inadequate, and harmful multiculturalist strategies in the Canadian education system. Predominantly white spaces within the system – such as the Canadian Teachers’ Federation, teacher union spaces shared with community organizations, spaces held between unions and their activist sub-groups therein, professional development spaces, and meeting spaces between administrators and teachers – must oppose and dismantle whiteness as a violent construct if education is to be a site of freedom, liberation, dignity, and humanity. This text examines Dumas’ theorization of anti-Blackness in education discourse and Leonardo and Porter’s Fanonian theory of safety in race dialogue, particularly their charge that white educators embrace “humanizing and liberatory” violence against white hegemony in education that currently enacts violence against racialized communities. The construct of whiteness and the privilege it provides white educational stakeholders are examined for their capacity to serve as an excuse for them to avoid dismantling oppressive structures that benefit them, or conversely, how they serve to unjustly center white people and marginalize people of color. Included within is an analysis of equity practices in Canadian school boards so that a comparison can be made between past, current, and imagined conditions. The question of whether the dominant group is willing to give up its power from which it greatly benefits is posed in order to discuss what is at stake in giving up that power as well as what opportunities arise for oppressed communities working to dismantle it. While I problematize, my perspective given my position as a white educator and researcher, I emphasize – based on the recommendations of other researchers – that crucial to transformative change in race dialogue is the effort to identify, locate, and condemn whiteness and systemic racism.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.038 | 0.111 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".