Bibliographic record
Abstract
I do not care what language a man speaks, or what religion he professes, if he is honest and law-abiding, if he will go on that land and make a living for himself and his family, he is a desirable settler for the Dominion of Canada…. If we can find people … willing to obey the laws and pay taxes for the support of our institutions, we must open our doors to these people and give them such encouragement as will overcome the initial difficulties of their change of situation. Clifford Sifton, Minister of the Interior, Statement to the House of Commons, July 1899. Indeed, not much has changed from Sifton’s statement over a century ago: Canada still needs immigrants. But we no longer need farmers to till vast expanses of land. Today we need IT specialists, production managers, researchers, carpenters, and tool and dye workers, to name a few, and we need them in our cities.1 Today it is the urban centers that are the engine and the lifeline of the national economy. Demographics have shifted radically, with almost 80 percent of Canadians living in urban centres in 2001 [Statistics Canada, 2002], as opposed to just over 80 percent living in rural areas in 1871 [Social Science Federation of Canada and Statistics Canada, 1983]. Concerns about labour force shortages dominate government policy circles, as well as
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.735 | 0.361 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".