Bibliographic record
Abstract
Québec’s engagement with the United States is the most significant and consequential point of interaction amongst all its international activities. This new, edited book volume seeks to explore the many ways in which Quebec engages with the United States, including political exchange, border issues, trade, business and investment, transportation, immigration, cultural links and identity, the role of energy transmission and natural resources, and environmental considerations. As a sub-national actor pursuing a wide range of paradiplomatic bilateral and multilateral initiatives directly involving the U.S., our book both explores and explains what, when, why and how Québec has chosen to engage the United States while examining the fundamental issues that lie at the heart of the relationship. Multidisciplinary and interdisciplinary in focus, this edited collection of essays, titled The Québec-United States Relationship: Political, Security, Economic, Environmental and Cultural Dynamics, features the work of scholars who think deeply about Quebec-U.S. relations. Each contribution considers contemporary policy relevant issues; in so doing, this collection examines and emphasizes the background, scope, and impacts of policy decisions. This is an open access book.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.772 | 0.499 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".