Canada-United States Supply Chain in the Era of Global Economic Competitiveness
Bibliographic record
Abstract
States Speaker -Paul Vandevert 8 assembly of one car can involve three, four, five border crossings.Producing each part in the most efficient location improves productivity, lowers costs, increases profits and that is the essence of today's global supply chain manager's challenge.9 Inefficiency, whether it is due to a lagging 3 Imports, exports and trade balance of goods on a balance-of-payments basis, by country or country grouping, Statistics Canada, available at http://www40.statcan.ca/101/cst01/gblec02a.htm (last visited Oct. 9, 2008); See also, Ontario Exports/Imports by Country, Ont.Econ.Dev., available at http://www.2ontario.com/welcome/ooit_317.asp(last visited Oct. 9, 2008).4 See generally Stephanie Fitch & Joann Muller, The Troll Under The Bridge, Forbes, at 134 (Nov.15, 2004).5 See Imports, exports and trade balance of goods on a balance-of-payments basis, by country or country grouping, supra note 3.6 See generally Samuel Palmisano, The Globally Integrated Enterprise, 85 Foreign Affairs 127, 127-136 (2006).7 See generally William Hoffman, Global supply chain?What global supply chain?;People talk about them, but BDP study says they're still relatively rare, J. Com.,
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".