Bibliographic record
Abstract
This study of the government finances of the Canadian province of Alberta implicitly examines the level of government intermediate between the federal and local governments in Canada and the United States.In the explicit sense, however, it is more than that.It is the story of a province affected by geographic and economic factors shared only with the other two prairie provinces and some western American states.For example, transportation costs bulk large in Alberta because of sheer geography, a condition which aggravates the cyclical and structural economic changes that have affected the province from time to time.The province was settled and developed rapidly in response to changes in demand and cost factors which made wheat growing both feasible and profitable on the prairies.Cyclical and structural changes in the demand for wheat and other agricultural products in the world's export markets during the 1920s and 1930s called for readjustments that created serious private and public financial problems.The increase in the demand for agricultural products during World War II led to rapid recovery from the Depression of the 1930s.Post-war developments in the petroleum industry have generated a high level of investment activity and provided the provincial government with greatly increased revenues.It is a story of ups and downs, of "feast" and "famine," of Cadillacs and "Bennett buggies."Two events are of special importance.One is the provincial public debt default and partial interest repudiation of 1936-46.The other is the emergence of Alberta as one of the "haves" of the ten provinces in the sphere of public finance.A discussion of the factors underlying these events, as well as of the events themselves, forms a considerable portion of this treatise.To deal adequately with these two developments as well as to examine the financial problems of the province as the intermediate unit of government in the Canadian federation, such factors as the geography of Alberta, the economic development of the province, its governmental structure and political development, expenditure and revenue policies, and local public finance are discussed at some length.Aside from the interpretation of data and the actual writing of the treatise, the main tasks which faced the writer were the selection of facts to be xxiii
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.305 | 0.113 |
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".