Populism and Social Policy: A Challenge to Neoliberalism, or a Complement to It?
Bibliographic record
Abstract
Do populists pursue distinct kinds of policies, and if so, how successful are those policies? Populist rhetoric often invokes themes of redistribution insofar as leaders claim that power and resources need to be restored to “the people.” As a result, populists tend to offer a very broad view of social policy that emphasizes security, order, rewards, and punishments. Populists’ narratives may be simple, but once in office, they may face complex problems that call for more sophisticated policy solutions. This study examines whether populist policies fit the messages they deliver to their target voters, and aims to contribute to the development of a methodology for determining that relationship in specific empirical cases. I focus on the case of Russia, which enacted a major change in its old‐age pension system in 2018 under the leadership of President Vladimir Putin.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".