Navigating the Free Trade—Fair Trade Fault-Lines by Michael Trebilcock
Bibliographic record
Abstract
The COVID-19 pandemic came with several revelations about our pre-pandemic lives. One of these revelations was the importance of international trade law in the lives of average individuals. Headlines about food supplies, shortages of essential medical supplies, and countries’ plans to acquire and produce vaccines dominated the media following March 2020. It was a time that spurred the public’s interest in international trade law and how it functions. Indeed, media headlines showcased the growing concern about international trade during the pandemic. This is the context in which Michael Trebilcock’s Navigating the Free Trade—Fair Trade Fault-Lines situates itself. At a time when everyday Canadians and others around the world were experiencing and reading about the effects of COVID-19, Free Trade—Fair Trade provides curious readers with a pithy, wide-ranging introduction to international trade law and its many challenges. Ultimately, Trebilcock convinces his readers that international trade law—and its impact on job availability and the price and availability of goods—can make a difference in people’s everyday lives.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".