Bibliographic record
Abstract
A HISTORICAL OUTLINE OF CANADA In its short history Canada has become a diverse and pluralistic modern society, both in terms of its geography and the contacts among its cultural groups. When the first European explorers – mainly from France and Britain – arrived on the east coast of Canada in the 1500s they found a land populated by less than half a million Aboriginal peoples. The Inuit lived along the coastal edges and islands of the Arctic; First Nations, or Aboriginal, peoples inhabited the rest of the land. As the population of French and British colonialists grew, settlers expanded westward for trade and land by colonial policy and war. Following the American Revolution of 1776, colonists loyal to Britain came up from America and settled in Canada. The eighteenth and nineteenth centuries saw a steady flow of British immigrants on the one hand, and Chinese, Italian, and Irish workers on the other. In the early twentieth century, many settlers originating from Eastern European countries came to the Canadian prairies to farm. After the 1930s, with the growth of cities, the tide of immigration flowed to urban centres, where the majority of Canadians resided and worked. Between 1946 and 1954, 96 percent of the immigrants admitted to Canada came from Europe. In the 1950s the federal government's immigration policy had been to fill the country's needs in the natural resource and industrial sectors; the policy later shifted toward acceptance of professionally educated workers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.028 |
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".