CANADA MACROECONOMIC BACKGROUND
Bibliographic record
Abstract
China and Italy. It accounts for about 2%of world out put. Per capita GDP in PPP terms is estimated at US$31,000. The performance of the Canadian economy is very closely linked with developments in the USA. Trade with the US accounts for 30 % of Canada’s GDP and it is therefore vulnerable to currency movements against the US$. Economic growth has been robust since the late 1990s (excepting a dip in 2001). Inflation is modest but unemployment, at 7.2%, while falling, is still relatively high by international standards. Interest rates have fallen significantly in the last 4 years from 5.8 % in 2000 to 2 % in 2004. DEMOGRAPHICS The 2001 Census estimates the Canadian population at about 32 million. This accounts for about 0.5 % of the world population. As in many other countries the rate of population growth is slowing and the population is ageing. Immigration is the main reason for growth. The rate of household formation is slowing from about 225,000 per year in the 1970s to around 150,000 in the late 1990s, but the proportion of single person households is also increasing offsetting this effect slightly. The average size of households fell from 3.9 persons in 1961 to 2.6 persons in 2001. HOUSING MARKET The 2001 Census estimates that the total residential dwelling stock in Canada is 12.5 million. UN data puts it
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.116 | 0.033 |
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".