An Update - Canadian Non-Energy Exports: Past Performance and Future Prospects
Bibliographic record
Abstract
In light of the fact that Canada was continuing to lose market share in the United States, Binette, de Munnik and Gouin-Bonenfant (2014) studied 31 Canadian non-energy export (NEX) categories to assess their individual performance. From this list, about half were expected to lead the recovery in exports. Since that time, NEX growth has picked up: about 80 per cent of the 31 categories have grown in line with, or outperformed, their respective U.S. benchmarks. Furthermore, about half are currently showing upward momentum. Many export categories highly sensitive to the exchange rate have been a key source of strength, as they benefited from the further depreciation of the Canadian dollar. In addition, a more granular analysis finds that export product categories are emerging or re-emerging from low levels. On the downside, however, some categories closely linked to commodity prices have been affected by weak activity and lower prices.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.038 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".