Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it