A Broader Conception of “Settler” States: The Impact of Immigration and Race
Bibliographic record
Abstract
Settler countries have become what they are through land theft, genocide, oppression of Indigenous People, and enslavement. Those who remain, including African descendants and Indigenous, continue to be seen as unable to attain the education or class status that would give them access to the “fruits of modernity” and are thus excluded from the opportunity to become equal citizens. What is critically important to our understanding of these processes is that they are not limited to “settler” societies like the US, Canada and Australia. Both settler colonial and European countries have histories of dehumanizing those who would come to their countries. The underlying question that I’m trying to address in this article is, what are the key factors driving the development of countries into nation-states with their current day immigration policies, and how those developments are impacted by historical processes of racialization. Theories that try to explain global migration flows often focus on South to North or South-South migration – however, a more global approach needs to include North-South migration that has impacted the development of countries throughout the Western hemisphere (and beyond).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".