Imagined Communities of 'Whiteness': Racial-Nationalist Origins of Settler-State Formation in Argentina and Canada, 1840-1914
Bibliographic record
Abstract
This dissertation sits at the intersection of critical international political economy and a decolonizing, anti-racist approach to empirical political science. Specifically, I examine how liberal state forms are presupposed by and premised upon illiberal practices of sorting, policing, and defining populations. Rather than view such practices as anomalous to the modern state form, I view them as productive. I depart from the dominant literature in this field of study (postcolonial theory) with a typical focus on discursive and local practices, and instead advance a defense of Marxism rooted in an examination of the material practices of states responding to global political-economic pressures. This analytical and methodological focus stems from an engagement with the theoretical and empirical work conducted through Political Marxism, and through an engagement with the concept of uneven and combined development. \n \nI compare instances of racialized nation-building from the nineteenth century, focusing on the ways in which the creation of racialized hierarchies of belonging were seminal to the production of liberal state capacity and legitimacy. I examine the cases of Canada and Argentina to explore how the dispossession and management of indigenous peoples served to foment vast networks of bureaucratic, fiduciary, and coercive state capacities. Such capacities were necessary in the project of constructing competitive liberal economies to respond to pressures generated by an emergent global market in agricultural goods. This work sheds new light on the role of race and racialization in the formation of the nation-state system, while responding to and contesting common assumptions about the legal equality assumed to underpin Western nationalism(s).
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 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 | 0.019 | 0.012 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".