Bibliographic record
Abstract
How widespread really is the language of left and right? One could recognize the clash about equality just described, but consider it largely a concern for experts and politicians, at a distance from the preoccupations and views of most people around the world. Outside the Western world, in particular, the left–right dichotomy may seem less relevant as a heuristic tool. This chapter uses global public opinion trends to demonstrate, on the contrary, that practically everywhere citizens understand this representation and position themselves along an axis going from left to right. The left–right cleavage is neither Western, nor passé . It is ubiquitous and very much contemporary. This chapter presents worldwide survey results that establish the near-universal relevance of the left–right division and its coherence for most people, who associate the two sides with the expected attitudes about equality, redistribution, and the role of the state. Country-specific data also confirms that these findings hold across very different regions and cultures of the world. The left–right debate is truly global. Indeed, in both national and comparative studies of public opinion, no cognitive instrument, no scale measuring personal values is more powerful than the way respondents locate themselves on the left–right continuum. Even scholars who claim that the left–right cleavage is in decline or in transformation cannot but conclude that it still incorporates most of the other attitude differences they seek to explain. This opposition is the most central value divide that political parties built as they struggled for, and about, democracy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".