By Patrick Grady, Global Economics Ltd. Why Worry About Industrial Structure?
Bibliographic record
Abstract
The central question I’d like to raise following up on Mike McCracken’s presentation on “Longer Term Industrial Structure Issues, ” and its projections of industrial output to 2030 is: “Why worry about industrial structure? ” And as a corollary: “What can we do about it?” The Longer Term Industrial Projections But before turning to my questions, there are a few things that need to be said about the projections. First, they certainly provide a good starting point for our discussions. They are built on a plausible set of assumptions on the key domestic and external factors including the demographics and the external environment. And thus they provide a reasonable and consistent basis for considering the evolution of Canada’s industrial structrue. But it is also important to bear in mind that forecasting is not a precise science and that projections have recently not been very reliable even in the short run. When private sector forecasters were surveyed before the February 2008 budget, the average of their forecasts for GDP growth in 2009 was for growth of 2.4 per cent (Chart 1). As it now seems likely, this is approximately the right magnitude but exactly the wrong sign. As a profession, we thus need to ask ourselves, how can we possibly hope to accurately forecast aggregate output 20 years in the
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.281 | 0.236 |
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".