Bibliographic record
Abstract
Globalization and democratization have added an increasing amount of complexity to the international system that theorists seek to define and forecast. It is no wonder then, that competing fields of thought all generally have some success in explaining certain aspects of it. However, for each success of a theoretical field, a failure, or counterexample, of the theory usually follows quickly behind. Realism since the Cold War has lost certain aspects of its predictive power, and both liberalism and constructivism are grappling with the large amount of factors that they recognize as potential variables in the international system. The problem with each of these is that they are too narrow. Instead of focusing on a complex synthesis of relatively equal factors, they try to pick one or two as causal forces and argue that the rest are effects. By redefining conflict in terms of a synthesis of relatively equal factors, we can get a better idea of which system is ideal for a “better off” world. The argument here is that U.S. unipolarity is constructed to mitigate almost all potential factors that disrupt security and stability, and also sets the stage for improvement of “smaller” human security issues, both of which contribute to a “better off” world.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".