Bibliographic record
Abstract
Building on the shift-share analysis of Barnett and Charbonneau (2015), this note decomposes Canada’s market shares in the United States, Europe and China for imports of non-energy goods into competitiveness, preference shifts and an interaction term. We find that, despite the depreciation of the dollar, Canada continued to lose market share over 2014–17 (around 0.4 percentage points lost per year on average over four years). Competitiveness losses are widespread among sectors and spread out over time. Losses from shifting preferences have stabilized since 2012 and are concentrated in motor vehicles and parts, forestry products, and building and packaging materials. Canada’s share of European and Chinese imports of non-energy goods has been stable at around 2 and 1 per cent, respectively. However, in each case, one key sector offsets competitiveness losses in a broad range of sectors. Canada’s exports in some markets have collapsed over recent years (e.g., small trucks, electrical apparatus for telephony), which suggests that some export capacity has been lost (or otherwise reallocated through a mandate change). In addition, we observe that Canada’s market share in US imports of services has been eroded by stiff competition from emerging markets, such as India. Finally, relative price movements appear to partly explain Canada’s lack of competitiveness in relation to Mexico and China for US imports of non-energy goods, more specifically in motor vehicles and parts and consumer goods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.045 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".