Working People in Alberta: A History
Bibliographic record
Abstract
ackn ow le dg e m e n t sWe thank the Alberta Federation of Labour and the Alberta Union of Provincial Employees for their financial support for the research and production of this book.They donated to the project with no strings attached.No official of either union body asked for or received an opportunity to read any of the chapters as they were written or before they were finalized.In short, the book is not an official history of any union organization, though it was timed to appear for the centennial celebration of the afl so as to contribute to fig 3-1 Chinese workers building the CPr main line, 1884.Library and Archives Canada, C-6686 b.What is this country coming to?Doukhobours pouring in by the thousands on the eastern slope, Galicians [Ukrainians] swarming over the central portions, and rats taking possession of Dawson City, one would imagine that Canada had become a veritable dumping ground for the refuse of civilization.7 The equating of rats that accompanied the Yukon gold rush with certain ethnic groups reflected the deep racism of many Britishorigin Albertans.Their careless stereotypes also ignored the fact that Alberta's ethnic workers included many who were skilled, literate, and politically sophisticated.For example, a study of the Polish community in the Crowsnest Pass found that young Polish immigrants, although coming from farming communities, had experience as carpenters, bricklayers, brewery workers, and tailors.They could read and write Polish.These were not the strong-of-body and weak-of-mind Eastern Europeans stereotyped by English Canadians.8 1 The 1885 Census boundaries for the District of Alberta excluded the northern third of the current province and a narrow band on the east, including Medicine Hat.The populations of First Nations are therefore understated, and the numbers elsewhere, while representative, are not exact.Census of the Three Provisional Districts of the North-West Territories: 1884-5, and Census of Canada, 1911, Vol. 1, Areas and Population by Provinces, Districts and Subdistricts.
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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 | 0.005 | 0.015 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".