Bibliographic record
Abstract
In 2003, the National Museums of Scotland organised an exhibition called Trailblazers the Scots in Canada*. Jenni Calder, a well-kent figure on the Edinburgh cultural scene, was one of the organisers, and Scots In Canada is a lively spin-off. It has two great strengths, a love of things Scottish and a feeling for Scots literature, both domestic and diaspora. The result is a historical evocation enriched by the creativity of imagination. (From a factgrinding historian, this is of course double-edged praise.) The structure of Jenni Calder's book takes us on a journey, which begins with the voyage, then introduces us to the initial Scottish engagement with the land of eastern Canada. A chapter called *Men Fare Well Enough* moves the story forward through the nineteenth century, portraying the Scots as successful pioneers. (The implied gender comparison in the title is not emphasised.) Then come two chapters on the West. Chapter Six, *The Right Sort for Canada*, doubles back a little to highlight Scots who made an impact in spheres such as politics and railway-building. Chapter Seven, T r u e Canadians*, probes the fundamental conundrum of national identity. This compact volume also includes illustrations, some well-drawn maps, a bibliography and a list of museums. It is an excellent souvenir volume.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".