Bibliographic record
Abstract
In 2011, business sector investment per worker in information and communications technology (ICT) in Canada was only 57.8 per cent of the U.S. level, indicating an ICT investment per worker gap of 42.2 percentage points. Numerous explanations have been advanced to explain this gap, one of which is that the ICT investment data from Statistics Canada and the Bureau of Economic Analysis are not strictly comparable. The primary focus of this report is to analyze that hypothesis. We compare the methodology used to measure ICT investment in Canada and the United States and find that issues related to measurement account for approximately 4 percentage points (10 per cent) of the gap. Although software investment has been responsible for 90 per cent of the gap in recent years, seven out of 17 industries in Canada actually had greater investment per worker levels than the United States in both total ICT and software. A small number of ICT-intensive industries has been responsible for a substantial part of the gap. In particular, information and cultural industries accounted for 39.1 per cent of the total gap. This supports the conclusion that the Canada-U.S. ICT investment per worker gap is largely the result of industry-specific factors which affect software investment.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".