How Should We Think about the Winners and Losers from Globalization? Three Narratives and Their Implications for the Redesign of International Economic Agreements
Bibliographic record
Abstract
Abstract In the wake of Donald Trump’s election to the US presidency, the ‘losers’ from globalization have received unprecedented attention. While few would contest that manufacturing workers in developed countries have lost out over the past decades, the remedies proposed by President Trump have been met with a mixture of concern and ridicule by the trade establishment. And, yet, it seems clear that, at least in the USA, politicians and trade officials are no longer able to convince voters that international economic agreements will ‘lift all boats’. Instead, those engaged in debates about trade policy will need to be open about the fact that international economic agreements create both winners and losers. This article identifies three narratives about who those winners and losers are. The article argues that the contestation between these three narratives is not one that can be resolved through empirical analysis but, instead, that the narratives contain irreducible normative elements. The article further explores the implications of these narratives for the redesign of international economic agreements.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".