Struggles Against Bilateral FTAs: Challenges for Transnational Global Justice Activism
Bibliographic record
Abstract
The past decade has seen major movements and mobilizations against the new crop of bilateral free trade and investment agreements being pursued by governments in the wake of the failure of global (World Trade Organization) and regional (e.g. Free Trade Area of the Americas) negotiations, and the defeat of an attempted Multilateral Agreement on Investment in the 1990s. However, in spite of much scholarly, non-governmental organization (NGO) and activist focus on transnational global justice activism, many of these movements, such as the major multi-sectoral popular struggle over the recently-concluded US-Korea Free Trade Agreement, are hardly acknowledged in North America and Europe. With a shift in emphasis pushing liberalization and deregulation of trade and investment increasingly favouring lower-profile bilateral agreements, this article maps the resistance movements to these latest shifts in global free market capitalist relations and discusses the disconnect between these (mainly Southern) struggles and dominant scholarly and NGO conceptions of global justice and the global justice movement as well as questions of knowledge production arising from these movements.
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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".