The urgency of the first link: Canada’s supply chain at breaking point, a national security issue.
Bibliographic record
Abstract
The creation of an intelligent supply chain is now an urgent national security priority that cannot be achieved without the joint mobilization of various stakeholders in Canada. It is not, however, an end in itself: the achievement of a single, competitive, sustainable, and consumer-focused domestic market should be the ultimate outcome of the national taskforce needed to collaboratively implement the recommendations of three complementary public policy reports published in 2022 on the state of the supply chain in Canada. The supply chain challenge is vast, and it will only become more complex over time. Governments in Canada must act together now, in conjunction with collaborative efforts with our allies and partners, notably the United States and the European Union, to ensure supply chain resilience in the face of accelerating current and anticipated upheavals, geopolitical conflicts and natural disasters. Québec's geostrategic position is a major asset, and gives it a critical role and responsibility in implementing not only the Final Report of the National Supply Chain Task Force ("ACT"), but also of the recommendations contained in the report published by the Council of Ministers Responsible for Transportation and Highway Safety (COMT) and those contained in the report of the House of Commons Standing Committee on Transport, Infrastructure and Communities published in Ottawa in November 2022, "Improving the Efficiency and Resilience of Canada's Supply Chains". The mobilizing approach towards a common data space for Canada's supply chain is inspired by Advantage St. Lawrence's forward-looking Smart Economic Corridor vision and builds on and integrates experience gained from various initiatives and programs implemented in Canada, the U.S. and Europe, as appropriate. Its initial implementation in the St. Lawrence - Great Lakes trade corridor will facilitate the subsequent access and sharing of data from across the Canadian supply chain in a reliable and secure manner. The accelerated joint development of a common data space is a game-changer not only in terms of solving critical supply chain challenges, but also in terms of the impetus it will generate in the pursuit of fundamental Canadian priorities, including the energy transition. This Bourgogne report offers a four-part synthesis: - An overview of a background characterized by numerous consultations, strategy announcements, measures, and mixed results. - A cross-analysis of the recommendations of three important and complementary public policy reports at federal level, as well as the Quebec strategy, “l'Avantage Saint-Laurent”. - An analysis of the fundamental issues of mobilization capacity, execution, and under-utilization of data. - Some operational solutions for moving into « Action, Collaboration and Transformation » (ACT) mode.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".