From Historical to “Dialectical” Populism: The Case of Post-Communist Romania
Bibliographic record
Abstract
This article begins with a brief discussion of the differences between “historical populism” and “neo-populism” in the post-communist context. The second part concentrates on “neo-populism from below,” exemplifying the category of neopopulist politicians aspiring to power, with the case in point being George Becali and his New Generation Party (PNG). The third part turns to “neo-populism from above,” that is to say, to populist policies employed by incumbent politicians for the purpose of preserving power and enlarging support. The example chosen here is that of President Traian Băsescu and the role played by intellectual elites in making possible “neopopulism from above.” The fourth examines successes and failures of the two categories of neo-populists in two electoral contests, namely the 2007 elections for the European Parliament and the 2008 local elections. It is pointed out that the latter electoral contest produced a prospective “populist dialectics,” namely, a symbiotic mergence of the two Romanian post-communist populisms. Finally, the fifth and last part turns to theoretical considerations of a general and comparative nature.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.033 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".