Bibliographic record
Abstract
As a way to consider the possibility of decolonizing discourses of diaspora, the central question posed in this paper asks not only where do people of the diaspora come from. In North America, nations have been superimposed on Indigenous lands and peoples through colonization and domination. Taking this relation seriously in the context of discourses of race, Indigeneity and diaspora within university classrooms interrupts business as usual and promises a richer analysis of one particular similiarity amongst diasporic, as well as settler, groups in North America with possible implications beyond this context. In short, the author asks each reader to respond to the question, “Whose traditional land are you on?” as a step in the long process of decolonizing our countries and our lives. While part of the focus for this paper is on theorizing diaspora, there are obvious implications for all people living in a colonized country. Drawing primarily on three pedagogical strategies and events arising from them, the author takes up some of the possibilities for theory-building that they suggest. Reflections on courses taught, student feedback and texts from Toni Morrison’s to James Clifford’s “Indigenous Articulations” ground the discussion.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".