Bibliographic record
Abstract
WE INTRODUCED THIS VOLUME by posing several questions about cultural, racial, and religious inclusiveness in rural Canada-a topic that, to date, has not received much scholarly attention.To what degree does cultural, racial, and/or religious intolerance exist in rural Canadian communities?What does such intolerance look like in rural Canada and how do such attitudes manifest?How should such attitudes be addressed in the context of the realities of rural life?What examples exist of organizations and individuals working to counter such attitudes and build more inclusive rural communities?How can institutions like schools, churches, co-ops, and community groups, so fundamental to everyday life in rural communities, play a central role in this work?And more generally, what do more inclusive rural communities look like?While we have not provided definitive answers, the volume's contributing authors have explored each of these questions in ways that provide much-needed starting points for future academic and practical work in this area.Taken together, the chapters in this volume provide distinct ways of considering the racial, cultural, and religious diversity that exists in rural Canada (a place more diverse than many realize); the attitudes that exist towards racial, cultural, and religious minorities and Indigenous peoples in rural Canada (attitudes that are often more complex than Conclusion
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.137 | 0.035 |
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".