Tracing E-race-sures, Finding Reclamations: Embodied Perspectives of “Canadian” Immersion
Bibliographic record
Abstract
At this critical time of reckoning with histories and present legacies in Canada, we come together as an emergent collaborative research team to reflect on how education and wider systems in this country have shaped our individual and collective experiences. As we affirm our practice of visiting and sharing histories from embodied perspectives and commitments in order to build relations, we find echoes in the concepts and practices of Métis and Black/African-Caribbean diasporic communities that we are in relation to. The notion of e-race-sures helps us name the gaps that our communities have experienced in the cultural imaginaries and literal making of Canada where racialized notions of belonging have enabled colonization and entitled (largely white) settlement. Beyond remediating these gaps, the notion of reclamations allows us to move past deficiencies and affirm what has always been there. This move facilitates thinking and acting accountably in relations that exceed what is underwritten by a seemingly coherent history and present story of Canada. By sharing our individual stories in places in the text, the authors name how we each come to know e-race-sures and reclamations in our own lives. But in collectivizing our stories and finding common resonances, we insist on the power of coalitional possibilities and on the need to make room for other communities and stories beyond ours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".