Settler Colonialism, Illiberal Memory, and German-Canadian Hate Networks in the Twentieth and Twenty-first Centuries
Bibliographic record
Abstract
Abstract This article is part of the collaborative research project Populist Publics. Housed at Carleton University ( www.carleton.ca/populistpublics ), it applies a data-driven analysis of online hate networks to trace how false framings of the historical past, what we call historical misinformation, circulates across platforms, shaping the politics of the center alongside the fringes. We cull large datasets from social media platforms and run them through a variety of different programs to help visualize how harmful speech and civilizational rhetoric about race, ethnicity, immigration, multiculturalism, gender equality, and LGBTQ+ rights are circulated by far-right groups across borders, noting specifically when and how they are taken up in the mainstream as legitimate discourse. Our interest is in how the distortion of the historical record is used to build alternative collective memories of the past so as to undermine minority rights and cultures in the present. We began with a basic question: To what extent is this actually new? As much as the atomized publics of our current day create ideal conditions for radical ideas to fester and circulate, it was obvious to us that we needed to look for linkages across time, drawing on interdisciplinary methods from the fields of history, media and communication, and data science to identify the tactics, strategies, and repertoires among such groups and individuals. By analyzing German-Canadian relations in particular, what follows is a first attempt to piece together some of these connections, with a focus on far-right hate groups—homegrown and imported—in the settler colonial project that is today's Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".