Bibliographic record
Abstract
The left–right distinction, observe political scientists Michael McDonald, Silvia Mendes, and Myunghee Kim, is the “core currency of political exchange.” Like price and quantity in economic exchange, these notions provide a simple and universal language that helps citizens, politicians, and experts make sense of politics. Without them, collective decisions and popular control over elected politicians would be very difficult, and indeed almost impossible. Intercultural and international debates would also lose much coherence and become hard to comprehend. But is the left–right distinction powerful enough to extend beyond the politics of equality and distribution it usually captures, and help us understand other global issues? And what does this language mean for the study of global politics? The first question concerns the heuristic value of the left–right dichotomy. Even among those who would agree that the cleavage between the left and the right is an enduring and encompassing one, many would contend that, in the end, it remains too simplistic to account for the complex universe of contemporary world politics. Many issues, they would argue, simply reflect other divisions, outside left–right dynamics. There is no denying that global politics does not begin and end with the left and the right. Religious differences, for instance, have been and remain a major source of conflict in the world. Still, as the core currency of political exchange, the left–right cleavage covers and shapes most questions.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".