Trade fetishism and the trade justice ratchet: between token and substantive change in NAFTA 2.0
Bibliographic record
Abstract
Countless socially responsible trade initiatives have emerged in recent years offering an uncertain mixture of token and substantive changes.After decades of battles over free trade, this marks a significant shift, challenging established debates over free versus regulated markets by promoting labour, gender, human, and environmental rights through trade agreements.This reorientation contains complex contradictions, with trade justice groups conceding to the popularity of trade while simultaneously insisting on a new vision of what trade is 'about.'Drawing on the idea of trade fetishism, this article argues that the desire for trade involves not only its material motivations, but its seductive content as a fetishised object of global capital, offering the fantasy of 'trade' as a symbolic source of pleasure.Through the case of the new NAFTA 2.0, it points to the relevance of trade politics that aspires not to overcome trade fetishism, but, as Lucas Pohl (2022) suggests, to 'get with' it.Through a trade justice ratchet mechanism, advocates have pushed for unanticipated changes, while also ceding to the limitations of the current order.The outcome is a process of contesting the symbolic content of what trade is and is not about, with significant material and policy implications.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.065 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".