Bibliographic record
Abstract
Over the past two decades there has occurred a shift in economic power from central Canada to other parts of the country. Saskatchewan and Newfoundland and Labrador have both claimed a noticeably larger share of Canada’s GDP since 1995 but easily the largest shift of economic output has been to Alberta. This adjustment in the Canadian economy is most easily observed in the large migration between provinces of Canadians seeking employment. Data from Statistics Canada’s Labour Force Survey shows that over the period 1995-2014 Alberta has maintained an average annual rate of growth in employment of 2.50 per cent. This is well above the 1.44 percentage rate of employment growth in second-place Ontario and double the average rate of growth in neighbouring British Columbia. This begs the question: What would Canada’s unemployment rate be today if Alberta’s job creation boom hadn’t happened? Since the national jobless rate is a weighted average of the provincial figures, getting an answer is straightforward. Assume Alberta’s employment growth was no higher than Ontario’s over the same period and the impact on Canada’s unemployment rate is startling. By August 2014, Canada’s unemployment would have been 9.39 per cent — 2.23 percentage points higher than the real figure of 7.16 per cent — and the Alberta economy would have created 411,000 fewer jobs; jobs which typically pay $200 to $300 per week more than jobs in Ontario and Quebec. This gloomy scenario means that Canada’s present unemployment rate would be 2.5 percentage points higher than it was in mid-2000, and 411,000 Canadians, along with their dependents, would be clearly much worse off were it not for the boom in Alberta. Obviously this simple experiment can’t capture the situation’s full economic complexity. Would some of those jobs have cropped up in other provinces? Stubbornly lacklustre growth could very well have forced governments and the Bank of Canada to adopt desperate measures; it could also have damaged postrecession recovery by increasing the federal budget deficit and limiting the Bank’s room to manoeuvre. While admittedly simple, this exercise highlights how reliant is Canada’s international reputation for economic strength and fiscal parsimony on Alberta’s prolonged economic boom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".