Bibliographic record
Abstract
Beneath, Beyond, or Within North America's Regional Box: Paradigm Indigestion? North America's Regionalisms Face Globalism: Integrative Climate Change Responses M.Lopez Vallejo Olvera Mexico-Canada Biotech Image: The Role of the Broader Scientific Community E.Antal & C.Tigau North American Anti-Immigration Rhetoric: Continental Circulation and Global Resonance of Discursive Integration L.Gilbert Integration through NAFTA's Chapter XI: Eroding Federalism and Regionalism? I.McKinley Thick Borders and the Challenge of North American Policy Coordination Post-Bush: What's Next? D.Drache Three Amigos and a Non-Regional Player: China as a Challenge Inside and Outside the NAFTA Box F.Haro Natural Resources and Out-Migration in Local Communities of Southern Mexico: Non-NAFTA Issues Impacting NAFTA A.Gonzalez Jacome Gendering NAFTA: Utopian Vision? R.Villanueva Ulfgard Developing National and International Civic Engagement Networks: The International Consortium for Higher Education, Civic Responsibility, and Democracy F.Plantan, Jr. North American Integration and Recession: Changing Order? I.Hussain The 2009 H1N1 Outbreak: A Chaotic North American Trigger with Evolving Global Consequences T.Lynch & P.M.Cox Conclusions: Rising to the Occasion: Coordinating Tumult
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".