Trade Policy and Politics: From Comparative Advantage to Trade Gamble
Bibliographic record
Abstract
To analyse trade and development through the disciplinary lens of neoclassical economics and its understanding of free trade and ‘comparative advantage’ requires making the initial assumption that trade policy, trade patterns, and trade outcomes are not significantly impacted or determined by unequal power relations between rich and poorer states; by domestic and global disparities and struggles around class, race, and gender; by historical legacies of colonialism, slavery, and imperialism; by political, ideological, and cultural institutions that pervade everyday life; by warfare, genocide, military invasions, and social violence; by corporate advertising, political funding, and media dominance; by complex implicit and explicit rules, norms, laws, and customs exercised through states and international regimes; by the ecological limits of industrialization and endless accumulation; by the real and perceived geostrategic interests of states in a competitive international ‘arena’; and by complicated, multidimensional, often obscure and unpredictable human behaviour, combined with the volatility and highly contingent nature of local and global markets. Once these factors are dispensed with, one can use the models of comparative advantage to develop and deliver trade policy. If the policy then fails, or if political and economic elites fail to fully adhere to the proffered prescriptions, as is so often the case, the above factors can be brought in at the end; the various failures attributed to ‘politics’ or ‘ideology’, which wrecked what otherwise could have been a smooth functioning project (e.g. see Stiglitz 2002, Sachs 2005, Bhagwati 2008). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".