Proactive economic policy and logistics: Quebec and continental supply chains.
Bibliographic record
Abstract
In industrial and consumer goods logistics, the era of several small distribution centers is gradually giving way to sets of continental distribution centers. The Netherlands, where a government policy fostered the implementation of numerous distribution centers with Europe-wide mandates, is a good example of this trend: manufacturers concentrated their distribution systems in one large center rather than managing several smaller centers. This rationalization does not equate with simplification, as continental distribution centers must handle high levels of orders, market adaptation and accurate delivery to a larger market. In fact, the complexity of the task stresses the need, for the manufacturer or the logistics service firm it may elect to subcontract, to carefully select the right location for the distribution center. Trying to take advantage of this global trend, the Quebec Government, in partnership with the Canadian federal government and private sector firms, decided to promote the setting up of distribution centers from Europe in the Montreal area. The partners listed a whole series of advantage for firms to set up in Montreal, and discovered there also was a strong cost-advantage for this specific location. This seemed to fit well with common wisdom that circulated among the 1 partners that cost was a paramount criterion for location decision.1 However, after five years of promoting Montreal as a choice location for distribution centers (DC), the government became disappointed since few firms had elected to establish their continental DC there. This discussion intends to show that rational and economically sound arguments do not make up a success in transportation policy, since other factors also interfere in the decision process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".