Bibliographic record
Abstract
These remarks are derived from a forthcoming work considering the future of international trade law. Compared with most features of the international legal system, the regional and bilateral trade law system is in the early stages of its evolution. For example, the United States is a party to fourteen free trade agreements currently in force, all but two of which have entered into force since 2000. The recent proliferation of agreements, particularly bilateral and regional agreements, is not unique to the United States. The European Union recently concluded trade agreement negotiations with Canada, Singapore, and Vietnam to add to its twenty-seven agreements in force and is negotiating approximately ten additional bilateral or multilateral agreements. In the Asia-Pacific Region, the number of regional and bilateral free trade agreements has grown exponentially since the conclusion of the Association of Southeast Asian Nations (ASEAN) Free Trade Area of 1992. At that time, the region counted five such agreements in force. Today, the number totals 140 with another seventy-nine under negotiation or awaiting entry into force. The People's Republic of China is negotiating half a dozen bilateral trade agreements at present to top off the sixteen already in effect. India likewise is engaged in at least ten trade agreement negotiations. The World Trade Organization (WTO) reports 267 agreements of this sort in force among its members as of July 1, 2016.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".