An Empirical Study Using the Heckscher-Ohlin Theory of International Trade and its Application to Canada’s Lumber, Maple, and Biofuel Industries
Bibliographic record
Abstract
Contained within this paper is a study that first addresses literature related to the Heckscher-Ohlin theory, and different variations and subsequent models that were derived from it. It seeks to understand the arguments for and against that are behind the assumptions the Heckscher-Ohlin theory makes for a mutually beneficial international trade. Beyond that, there is an empirical evaluation of Canada’s international trade as it applies to international economics through the lens of the Heckscher-Ohlin theory. The empirical evaluation seeks to prove the validity of the Heckscher-Ohlin theory as it applies to Canada’s vast lumber industry, and how due to Canada being the second largest exporter of lumber and having the third most forest coverage in the world, it may validate the Heckscher-Ohlin theory. We then seek to prove that the Heckscher-Ohlin theory is valid when applied to Canada’s notoriously vast maple industry. This is conducted through empirical data available on the country of Canada as a whole and then put under a closer lens through the province of Quebec which produces an overwhelming majority of Canada’s maple syrup. After understanding the maple industry in Canada, we then turn our view to Canada’s biofuel industry, and the ease of application of how their abundant agriculture is used to produce vast amounts of biofuel exports. The paper is organized as follows: After introduction section, Section 1 does the literature survey about applicability of Heckscher-Ohlin theorem. Sections 2 is the empirical test of the theorem for Canada’s trade experience and Section 3 makes the summary and conclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".