Bibliographic record
Abstract
During the last decade, most industrialized countries have acknowledged that high-technology industries are important for their social and economic well-being (OECD, 1988). Both the public and the private sectors have commissioned many studies to find ways of developing new high-tech industries and accelerating the growth of existing ones (Brainard, 1988). However, no standard definition of high-tech industries exists: it varies from study to study depending on the objectives (Markusen et al., 1986). Moreover, little empirical research has been published on defining the Canadian high-tech industry except for the work of the Economic Council of Canada. It is thus not surprising that there is no consensus on the definition of "high-tech industries". This article reviews some of the current concepts and definitions. It also examines recent employment and earnings trends in Canadian high-tech industries. A modified Economic Council of Canada definition of high-tech industry is used in this study. Definitions of high-technology industry The term "high technology " or "high-tech " is commonly used, but what exactly does it mean? What are its unique features and characteristics? To some people, high-tech means industries that devote much of their resources to research and development. To others, it may refer to industries that manufacture innovative and technologically advanced products (for example, new pharmaceutical products, aerospace or electronic equipment). Sometimes high-tech implies state-of-the-art processing techniques using robotic, computer-aided manufacturing or laser technology. Or, it may simply refer to industries that make fashionable consumer goods and services such as "high-tech " tennis racquets and athletic shoes, health care and cosmetic products. The lack of a standard definition may lie in the very nature of high-tech industries. Many characteristics of high-tech industries are qualitative. It is difficult to devise scales or measurement systems to capture, for example, accelerated obsolescence, high risk or strategic importance to government. High-tech characteristics may also change quickly, influenced by market forces, public policy and technology itself
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.202 | 0.087 |
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".