Unlearning: A messy and complex journey with Canadian Foreign Policy
Bibliographic record
Abstract
Adopting a narrative approach, I describe how doing research on the Highway of Tears, which exposed me to Indigenous method and theory, required of me an unlearning of core assumptions about who I was as a scholar. In addition, the ongoing process of unlearning has only reinforced my view that we must be mindful about the ways in which the field of Canadian Foreign Policy (CFP) has the potential to construct images of Canada that marginalize francophone, feminist, and Indigenous voices and perspectives. We need to embrace the complexity of our country and tell stories that problematize dominant, and often simplistic, narratives.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.110 | 0.062 |
| Scholarly communication | 0.038 | 0.011 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".