Bibliographic record
Abstract
Abstract Relative wealth inequality between countries of the North and South has not improved since the era of decolonization, yet the LIO's economic regime has scarcely been challenged since the 1970s' New International Economic Order. This paper seeks to explain this puzzle by theorizing and empirically tracking a pervasive pattern of rhetorical “domestication” through which wealth inequality was framed as a domestic instead of an international problem. As part of a rhetorical process of “containment,” the NIEO challenge was met with two alternative, liberal discourses from the 1980s through the present: a “responsive” discourse embodied by the Brandt report and its social-democratic middle ground; and a “resisting” one typified by a speech delivered by Ronald Reagan in Cancun in 1983. Our empirical demonstration illustrates how LIO proponents discursively contained NIEO contestation through the spread of a domesticated rhetoric. Using a corpus of General Assembly annual debates from 1971 to 2018, our machine learning textual analysis reveals how a growing proportion of diverse countries address economic development in an increasingly managerial way. By tracking rhetorical tropes, we document a groundswell movement away from structural and political contestation of the LIO. Overall, our original methodology—based on an inductive and relational approach to machine learning text analysis—allows us to capture the many euphemisms that containment diplomacy at the UN entails, and more generally, how key political problems get muffled in global debates.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".