CENTRE FOR THE STUDY OF LIVING STANDARDS OVERVIEW OF DEVELOPMENTS IN ICT
Bibliographic record
Abstract
nominal and real ICT investment growth in the total economy and how the three components of ICT investment- computers, communication equipment, and software- have contributed to this growth. The following summary highlights the key findings of this report: In 2012, nominal (current dollar) total ICT investment spending in Canada rose 3.3 per cent to $43.4 billion; this rate of growth was identical to the 2011 rate (3.3 per cent) and below the 2010 rate (4.2 per cent). Nominal total ICT investment growth was tepid in the 2008-2012 period relative to the 2000-2008 period. In fact, nominal total ICT investment grew at a compound annual average rate of 1.1 per cent during the 2008-2012 period, half of compound annual average rate experienced over the 2000-2008 period (2.8 per cent). Nominal total ICT investment was up 2.6 per cent in the business sector to $33.7 billion in 2012, while it grew 5.9 per cent to $9.7 billion the non-business sector. The business sector contributed 2.0 percentage points to the growth of nominal total ICT investment in the total economy, while the non-business sector contributed 1.3 percentage points. In 2012, nominal computer ICT investment grew by 1.7 per cent to $12.7 billion, nominal communication equipment ICT investment grew by 5.4 per cent to $8.8 billion, and
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.100 | 0.028 |
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".