Lagging Behind: Productivity and the Good Fortune of Canadian Provinces
Bibliographic record
Abstract
The good fortune of bountiful natural resources is not enough to ensure rising incomes for Canadians in the long term. Growing labour productivity is the most important determinant of future economic welfare and on that measure, Canada is falling behind its major trading partners. Increasing labour productivity does not mean workers working harder for less money, a common canard. It means more investment in one of three factors: 1) human capital (education or other learning); 2) physical capital (plants or other infrastructure); or 3) technology. Just as an individual’s income is in the long-run dependent on how productive he or she is, so too is that of the nation as a whole. If Canada fails to improve its productivity, the incomes of both individual Canadians and the nation as a whole will fall behind those of other developed countries.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".