Bibliographic record
Abstract
Estimates of the quantitative attributes of Canadian economic history, such as total output, manufacturing output, labour productivity, and price changes, have, with the application of more sophisticated methods of data collection and compilation, undergone significant revision since the 1950s. These revisions, which remain ongoing, have had, in turn, significant implications for scholars' understanding of the growth and development of the Canadian economy … In this article, I focus on the various data sets that either implicitly or explicitly underlie scholars' understanding of Canadian economic growth and development from Confederation (1867) to the late 1920s. Of particular importance are the different measures of real, or constant dollar, Canadian gross national product (GNP)(total output produced for sale on the market) and related estimates that are now available. More specifically, I address how these relate to the available estimates for population, immigration, exports, imports, investment, and real income. Different sets of estimates lend support to differing interpretations and explanations of Canadian growth and development. This debate has focused on the pre-1925 years because, from 1926 onwards, the Dominion Bureau of Statistics, the agency that evolved into Statistics Canada, produced consistent and almost indisputable annual estimates for a large array of variables, including gross national product. For this later period, debate concentrates upon alternative explanations of the path and pattern of Canadian growth mapped out by the estimates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".