Bibliographic record
Abstract
Abstract Structural shifts in patterns of international production, trade, and investment have been spurred by the globalization of supply chains. Today, however, a range of factors, including wars and sanctions, the increasing divide between Western and Chinese approaches to the economy, the increasing links between economic security and national security, and between trade and environmental regulation, are contributing to the fragmentation of international economic frameworks and growing populist and protectionist sentiment. Competition for advantage through industrial subsidies and the setting of digital and environmental standards risks enshrining a less-than-global economic order. In many fields, however, multilateral cooperation, rulemaking, and enforcement in serving shared objectives, in various international organizations, multi-stakeholder forums, and by the private sector, continues to function well. In this ever-more complex international context, where clubs, coalitions, and informal networks among nations are increasingly prevalent, middle powers are wise to remain both agile and resolute in ensuring that national interests are advanced through smart diplomacy, encompassing working with like-minded in caucuses and informal settings that serve to build broader consensus, engaging business and other stakeholders at home and abroad in support. Canada’s experience and current efforts provide a constructive example for middle powers.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".