Long-term Trends of Production Technologies in Canada:
Bibliographic record
Abstract
This paper explores empirically the long-term trends in production technologies of the Canadian economy, using the input–output data for the period 1961 to 2000. According to the industry technology concept of input–output analysis, each industry chooses its human and material ‘inputs ’ into its production processes and the shares of such inputs to the total spending depict the technologies employed by the corresponding industries. As such, long-term shifts in these input shares reveal trends of production technologies. This study draws on this concept to explore the technological characteristics of industrial production in the Canadian economy over those forty years. The Canadian economy is studied under three segments, namely (a) goods-producing industries, (b) services-producing industries, and(c) government sector industries. The inputs used in the production activities of these industry segments are analysed in a three-dimensional setting covering a four decade period from 1961 to 2000. The study demonstrated, among other things, that an economic structural change had occurred in the Canadian economy during the 1980s with a substantial decline in the share of goods-producing industries and a corresponding growth in the share of service-
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".