Bibliographic record
Abstract
Preface Implications of the COMESA Free Trade Area & the Proposed Customs Union U.S. China Trade -- Eliminating Non-market Economy Methodology Would Lower Antidumping Duties for Some Chinese Companies South Korea -- U.S. Economic Relations: Co-operation, Friction & Prospects for a Free Trade Agreement (FTA) Brazilian Trade Policy & the United States Generalized System of Preferences: Background & Renewal Debate A History of Canada -- United States Trade Relations A Quantitative Assessment of the Inter-War Australian Trade Policies Using the CGE Approach Economic Policy Challenges of Gains from Trade: The Case of Austria U.S. Agricultural Trade: Trends, Composition, Direction, & Policy Free Trade Agreements: Impact on U.S. Trade & Implications for U.S. Trade Policy Skill Distribution & Systems of Cities in a North-South Trade Model Organic Page Information for International Trade & Finance Middle East Free Trade Area: Progress Report U.S. International Trade: Data & Forecasts Regional Trade Blocks in a Non-Cooperative Network: Why Are Trade Agreements Regional? A Theory Based on Non-Cooperative Networks Index.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".