Economic Crisis, Polarization, and Prime Ministerial Approval in Greece
Bibliographic record
Abstract
Abstract This chapter has two goals. The first is to investigate and analyse, for the first time, the driving factors of prime ministerial approval in Greece. Second, to explore whether and to what extent these drivers change over time. To address these questions, a unique quarterly time series on Greek prime ministerial popularity, covering the period first quarter 1998 until second quarter 2019, is used. Economic conditions together with political factors are employed as independent variables. The findings suggest that subjective economic factors had little effect on prime ministerial approval, while party polarization on economic issues, like the bailout agreement, registered a strong negative effect. Moreover, it appears that economic polarization weakens the strength of economic evaluations effect. The findings also suggest that future research on popularity functions should emphasize more the role of political factors by further investigating the interaction between the political and economic cycles. Greece represents a good example for analysing the dynamic aspects of the above interactions using different statistical models based on both subjective and objective sets of variables.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".