Bibliographic record
Abstract
CULTURAL RECKONING.On one hand, the last decade has witnessed a surge in popularity of extreme rightwing movements, many inherently nativist and xenophobic, in parts of Europe and the United States (Akkerman, de Lange, and Rooduijn 2016; Hainsworth 2016).Although such movements have so far failed to gain a strong foothold in Canada, recent research does point to increasing occurrences of "hate group" activity (Perry and Scrivens 2016) and specific incidents of "hate crimes" across Canada (Leber 2017).Researchers have also noted weakening levels of popular support for immigrants and refugees (Donnelly 2017), including a growing suspicion of Muslim Canadians in particular (Sevunts 2016).Meanwhile, lack of sympathy for the historical and ongoing challenges faced by Indigenous peoples in settler-colonial Canada remains widespread, although this is beginning to change (Environics Institute for Survey Research 2016; Angus Reid Institute 2018).On the other hand, mass protests across the United States in response to numerous instances of police brutality against Black Americans in the summer of 2020, coupled with the ongoing work of Canadian scholars like Marie Battiste (2011, 2017), Robyn Maynard (2017), and Rinaldo Walcott (2003), among many others, have seemingly awakened a parallel desire Inclusivity is also essential to rural communities for practical reasons.The future of many rural communities is largely dependent on their ability to attract and retain immigrants (Caldwell et al. 2016; Carter, Morrish, and Amoyaw 2008; Ouattara and Tranchant 2007; Reimer 2007; Wiginton 2013; Yoshida and Ramos 2013).Furthermore, maximizing vis-à-vis the urban majority and in which rural citizens experience "a strong sense of identity as a rural person combined with a strong sense that rural areas are the victims of injustice."Research in the United States leaves little doubt as to the connection that exists today between rural citizens' sense of being "left behind" economically and culturally and the growing resentment aimed at minority populations (Hochschild what is, in fact, a very complex reality.It ignores, for instance, the widespread efforts of municipalities, community groups, spiritual leaders, and schools across rural Canada to counter intolerance in their communities.It overlooks the fact that rural Canada is dotted with numerous Welcoming and Inclusive Community committees, Indigenous-settler friendship initiatives, and dozens upon dozens of other groups, religious and secular, dedicated to sponsoring and supporting the resettlement of refugee families from war-torn regions across the globe.The assumptions often made about rural Canada further disregard the deeply embedded
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.209 | 0.094 |
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".