An approach to plan infrastructural investments to facilitate domestic trade
Bibliographic record
Abstract
The volume of trade between two regions is shaped by the cost of trade, which is a function of infrastructural and political barriers. Governments can invest in the upgrade of their country's logistics infrastructure to mitigate these barriers, decrease trade costs, and increase domestic trade volumes; but they need to decide which infrastructural project(s) to invest under limited budget. A bilevel, bicriteria optimization model is proposed to overcome this challenge. The inner model anticipates total trade flows among all regions of the country with a profit maximization lens of the transporters. The outer model selects the projects based on minimizing total investment costs and maximizing total trade flows. The novelty in this approach is in incorporating the implicit 'trade barriers' through a 'trade cost model' borrowed from the economics literature. The proposed framework is implemented in a case study originating from the Canadian Northern Corridor concept which aims to boost domestic trade between the provinces and the territories of Canada. The numerical study indicates that investments to upgrade and increase connectivity in the logistics infrastructure of western Canada should be prioritized over others.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".