Bibliographic record
Abstract
Canada’s living standards have been falling relative to those in the United States in recent years. The Chairman and CEO of the TD Bank Financial Group, Charles Baillie (2001) has suggested that Canadians adopt as a societal goal not only the reversal of this downward trend, but that Canadian living standards exceed US living standards within 15 years. Policies that the public and private sectors might adopt to attain this very ambitious objective were the focus at the multi-stakeholder roundtable organized for October 7-8, 2002. The objective of this background paper is to provide a framework for analysis and discussion of the issue of raising Canadian living standards. The paper first discusses definitions of living standards and related concepts. It then examines trends of living standards historically in Canada and the United States and in OECD countries. The third section looks at the relative importance of the determinants of living standards – productivity, working time, demographic structures, labour force participation, and the unemployment rate – in the growth of living standards in Canada and in accounting for the income gap with the United States and other countries. The fourth section discusses what strategies need to be pursued in terms of the five determinants of living standards growth for Canada to exceed US living standards by 2016. The key conclusions of the paper are twofold. First, a focus on improving Canada’s productivity growth performance, and in particular, eliminating the Canada-US productivity gap, is by far the most important and effective way to attain the objective of Canadian living standards exceeding US living standards by 2016. Second, an objective for Canada of matching or exceeding the US productivity level is probably a better societal objective than equaling or exceeding US living standards, as measured by GDP per capita. Attaining this objective would certainly give Canadians the opportunity to have the same level of per capita income as Americans, but it would also give Canadians the option of choosing more leisure time, a component of economic well-being that is currently not incorporated into GDP.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".