Bibliographic record
Abstract
In 2005, the CSLS published a report that examined spending on information and communication technology (ICT) in Canada and the United States between 1987 and 2004. It found that Canadian firms lagged considerably behind US firms in ICT spending and that this situation accounted to some extent for the lower labour productivity growth experienced in Canada. This report provides an overview of the latest developments using the most recent update of the CSLS ICT database. It finds that ICT investment spending in the United States in 2005 and 2006 continued to outpace that in Canada, increasing an average of 5.6 per cent annually in the United States compared to 3.3 per cent in Canada when expressed in current dollars. Following this trend, nominal ICT investment per worker in domestic currencies also grew faster in the United States than in Canada in 2005 and 2006, 3.7 per cent versus 1.6 per cent. The recent increase in the Canadian dollar, however, lead to a sharper decrease in ICT prices in Canada than in the United States over the 2004-2006 period. This in turn led to an increase in the level of PPP-adjusted ICT investment per worker in Canada relative to the United States from 56.5 per cent in 2004 to 58.0 per cent in 2006, continuing the positive trend started in 2000 when it stood at 49.0 per cent. While Canada’s steady relative improvement since 2000 in terms of ICT investment per worker is encouraging, the low relative level of ICT investment per worker remains problematic and should be of concern to policy-makers as ICT investment is a key driver of productivity growth.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.067 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".