Bibliographic record
Abstract
The liberalization of international trade has always been associated with social, political and economic disruption. Indeed, such disruptions are the very source of much popular opposition to liberalization. The moment humans began trading with one another, anxieties were provoked about the impact of exotic foreign wares on everything, from local culture and politics to, of course, economic activity (Irwin 1996: 11–15). These anxieties have periodically, but regularly, spilled over into the kind of populist, anti-trade xenophobia infecting modern debates about trade. This chapter begins with a short discussion of how embedded the politically, economically and socially disruptive qualities of trade liberalization actually are. In part, this chapter will argue, there has always been a connection between trade and both labour and the environment. However, it was the NAFTA that permanently situated labour and environmental issues as meriting consideration in the governance of regional and global trade. Yet having labour and the environment on the agenda has produced uneven results. The NAFTA’s side agreements on labour and the environment may have started it all, but these same agreements are also emblematic of the uneven treatment and impact of labour and the environment on global trade. Indeed, the NAFTA side agreements were initially afterthoughts, born of the politics of the day and – much to the mutual chagrin of people on different sides of the debate around the nexus of trade, labour and the environment – designed more for show than substance. The result was that the labour half of the side agreements quickly went dormant while the environmental half survived and become more robust than their authors intended. In other words, few were entirely happy with the side agreements. Finally, this chapter will note the significant, but also surprising, degree to which the importance of labour and the environment was affirmed in the NAFTA’s successor, the United States–Mexico–Canada Agreement. Winners, losers and spillovers There are few certainties in life, but there are two that students of international trade can commit to memory: (a) trade liberalization creates “winners” and “losers”; and (b) the “winners” are broadly distributed and the “losers” are highly concentrated.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".