Assessing shifts in Canada's competitive exposure in its home markets
Bibliographic record
Abstract
Purpose The paper's aim is to analyze changes in the relative importance of Canada as a supplier for its home markets; and, the rising importance of China versus other Canadian trading partners. Design/methodology/approach The market overlap measure (MOM) statistic, developed by Sawchuk and Yerger is used to analyze the Canadian home market shares for Canada and every other nation with sales in the Canadian market for each of 61 different NAIC sectors (56 at the four‐digit NAIC level and five at the three‐digit NAIC level). Findings The USA remains the most important foreign supplier to Canadian markets with a weighted average 23.6 percent market share as of 2003 (Canadian‐based production having a 63.0 percent market share). US market share, however, has been declining by nearly a percentage point per year since 2000. Approximately, half of the lost US' market share has been captured by Canadian‐based firms and approximately a quarter has been captured by Chinese production. China's growth in Canadian market share places it second behind only the USA in terms of Canadian‐based firms' home market competitive exposure. Research limitations/implications The work does not include an analysis of service sector trade flows due to inadequate data. Practical implications The MOM statistic is shown to be a useful diagnostic tool for analyzing the level of, trends in, and industries driving the competitive exposure a nation's firms have on sales in a specified market. Originality/value The MOM statistic is shown to yield better insights regarding the actual degree of competitive exposure than does the more commonly used similarity indices such as Finger‐Kreinen.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".