Bibliographic record
Abstract
What comes to mind when we think of the Old West? Often, our conceptions are accompanied by as much mythology and mystique as fact or truth. What are the differences in how the Canadian and American Wests are perceived? Did they develop differently or are they just perceived differently? How do our conceptions influence our perceptions? A companion volume to One West, Two Myths: A Comparative Reader, One West, Two Myths II: Essays on Comparison presents scholarly views on the comparison of the Canadian and American Wests and the various methodologies involved. Contributors include literature specialists, scholars of popular culture, art historians, and political, social, and intellectual historians, demonstrating the interdisciplinary nature of this area of study. With Contributions By: J.M.S. Careless Sarah Carter Brian W. Dippie R. Douglas Francis C.L. Higham William H. Katerberg Lee Clark Mitchell Roger L. Nichols Robert Thacker Fredrick Jackson Turner Aritha van Herk David L. Williams
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".