Bibliographic record
Abstract
Canada’s productivity performance reflects in large part our innovation record, both in terms of business sector R&D and information and communications technology (ICT) investment. The objective of this report is to examine the country’s ICT investment performance since 2000. The key finding is that, since the 2008 peak, business sector ICT investment in Canada has performed poorly, both relative to the Canadian non-business sector and to the business sector in the United States. By 2013, four years after the 2009 recession, nominal ICT investment in the business sector in Canada had failed to regain the 2008 level, falling on average 1.0 per cent per year over the 2008-2013 period. In contrast, despite government belttightening, nominal investment in the non-business sector in Canada advanced at a 2.0 per cent average annual rate. Equally, the United States, which experienced a more severe downturn than Canada, saw business sector nominal ICT investment grow at a 1.5 per cent average annual rate between 2008 and 2013. The fall in nominal ICT investment in Canada, combined with the increase in the United States, resulted in an 8.5 percentage point fall in the ICT investment per worker in Canada from 59.6 per cent of that of the US business sector in 2008 to 51.1 per cent in 2013. More research is needed to understand the reasons for the weak post-2008 ICT investment performance of Canada’s business sector.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".