Racialization, colonialism, and imperialism: a critical autoethnography on the intersection of forced displacement and race in a settler colonial context
Bibliographic record
Abstract
Migration has been identified as a priority area for policy responses by both the federal and provincial/territorial governments yet, much of our knowledge about migration is not premised on addressing current xenophobic and racist narratives about migrants. The purpose of this research is an interrogation of Canada's colonialism, imperialism, and racialization, which produce specific oppressive policies and practices that have impacted my family. This research is premised on the understanding that in the space between what is known about migration in Canada and what is not, a great deal of narrative and interpretive work is done that makes assumptions about migrants, specifically forcibly displaced people from the Global South. Through a critical autoethnography focused on my lived experiences as a descendant of forcibly displaced Chinese-Vietnamese people living in a settler colonial nation state, this study critiques to what extent these assumptions are founded, and to what extent they represent a socio-political climate in which migration is set out as particular problems requiring a legal and policing solution. In particular, my analysis centers anti-colonialism and anti-racism, shifting to resistance to systemic violence and liberation, while considering the discursive and on-the-ground effects of racist, colonial, and imperial policies and practice. Set against the backdrop of the rise of white nationalism, xenophobia, and racism across all levels of government and academia, and the general public, the results of this study produce a counter-narrative focused on the intersection of forced displacement and race in a settler colonial context, which is both timely and urgent.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".