Assessing Alberta’s Industry Dependence on US Markets and Policy Responses to Protectionism
Bibliographic record
Abstract
This capstone assesses Alberta’s vulnerability to United States (US) protectionist trade policies by identifying which provincial industries are most dependent on the American market and evaluating policy options to reduce this reliance. While Alberta benefits from a close trading relationship with the US, accounting for nearly 90 percent of exports, this concentration exposes the province to significant economic and labour market risks. The analysis develops an industry-level “vulnerability report card” using 2021 Statistics Canada data, normalized across four categories: trade concentration and output exposure, economic value exposure, labour market exposure, and import exposure. Eight metrics were constructed and standardized on a 0–10 scale, with composite scores converted into academic-style letter grades. This approach enables direct comparison across industries of varying size and structure, highlighting both sectoral and systemic vulnerabilities. Overall, the province earned a composite score of 5.65, placing it in the moderately high dependence category (C grade). Vulnerability was most acute in oil and gas and manufacturing, with subsectors such as chemical, transportation equipment, wood products, and machinery receiving failing grades. Outside of manufacturing, forestry and logging also ranked among the most exposed industries. Policy recommendations focus on strengthening interprovincial trade and labour mobility, reducing small business tax burdens in exposed sectors, and removing the federal tanker ban to open new export routes. Together, these strategies aim to reduce Alberta’s structural reliance on the US and enhance long-term economic resilience.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".