Bibliographic record
Abstract
Since 1776, more than 100,000 Swedish-speaking immigrants have arrived in Canada from Sweden, Finland, Estonia, Ukraine, and the United States. Elinor Barr's Swedes in Canada is the definitive history of that immigrant experience. Active in almost every aspect of Canadian life, Swedish individuals and companies are responsible for the CN Tower, ships on the Great Lakes, and log buildings in Riding Mountain National Park. They have built railways and grain elevators all across the country, as well as churches and old folks' homes in their communities. At the national level, the introduction of cross-country skiing and the success of ParticipACTION can be attributed to Swedes. Despite this long list of accomplishments, Swedish ethnic consciousness in Canada has often been very low. Using extensive archival and demographic research, Barr explores both the impressive Swedish legacy in Canada and the reasons for their invisibility as an immigrant community
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".