Bibliographic record
Abstract
Alberta is undergoing a painful economic transition, and this book is well positioned to inform some of the critical debates concerning the province's financial future.Until about 2013, Alberta's economy had been outperforming the rest of Canada's for so long that it seemed a given.In 2005, TD Economics reported that GDP per capita in the Calgary-Edmonton corridor was a "gigantic" 47 percent above the Canadian average, as well as substantially above the average in the United States. 1 By the end of 2011, and despite the lingering effects of the global financial crisis, weekly earnings in Alberta had risen 4.5 percent over the previous year and wholesale trade was up by 17.1 percent, while unemployment was the lowest in the country-even though Alberta's population had climbed over the past year at a rate 70 percent above the national average. 2 The fall from these economic heights was dramatic.Alberta's GDP peaked in 2014, shrank over the next two years, recovered partially from 2017 to 2019, only to drop again in 2020 to a new low.3 Calgary and Edmonton vied with St. John's, Newfoundland, for the cities with the worst unemployment rates in the country.Population growth slowed markedly as interprovincial migration turned negative.4 Provincial finances faced an equivalent upheaval.In the 2010-11 fiscal year, the Alberta government had no net debt, and its AAA credit rating was the best among Canada's provinces.5 A decade later, the November 2020 fiscal update forecast an annual deficit of $21.3 billion.Total taxpayersupported debt was expected to reach $97.4 billion by 2021 and soar to https://
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".