The relationship between ICT investment and productivity in the Canadian economy: a review of the evidence,” CSLS Research Report 2006-05 (Ottawa: Centre for the Study of Living Standards
Bibliographic record
Abstract
The objective of this report is to shed light on the relationship between information and communications technologies (ICT) and productivity in the Canadian economy. The first part provides a detailed overview of ICT investment and capital by ICT component (computers, communications equipment, and software), and ICT investment and capital stock per worker by ICT component in Canada for the total economy and for 20 NAICS industries, focusing on trends over the 1980-2005 period. The second part contains a general discussion of ICT and productivity issues, with particular emphasis on the relationship between investment in ICT and productivity. The third part provides a review of the literature on the relationship between ICT and productivity, again with a particular emphasis on Canadian studies. The key conclusion of the report is that ICT has been the driving force behind the acceleration of productivity growth in Canada and the United States since 1996. However, the potential of ICT has not been fully exploited and we will continue to see significant ICT contributions to productivity growth in coming years. The role for government is to develop appropriate policy frameworks so that the productivity-enhancing effects of ICT can be fully realized.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".