Continent of pessimism or continent of realism? A multilevel study into the impact of macro-economic outcomes and political institutions on societal pessimism, European Union 2006–2012
Bibliographic record
Abstract
The often-posed claim that Europe is a pessimistic continent is not unjustified. In 2012, 53 percent of European Union (EU) citizens were pessimistic about their country. Surprisingly, however, societal pessimism has received very little scientific attention. In this article, we examine to what extent political and economic factors drive societal pessimism. In terms of political factors, we expect that supranationalization, political instability, and corruption increase societal pessimism, as they diminish national political power and can inspire collective powerlessness. Economically, we expect that the retrenchment of welfare state provisions and economic decline drive societal pessimism, as these developments contribute to socioeconomic vulnerability. We assess the impact of these political and economic factors on the level of societal pessimism in the EU, both cross-nationally and over time, through multilevel analyses of Eurobarometer data (13 waves between 2006 and 2012 in 23 EU countries). Our findings show that the political factors (changes in government, corruption) primarily explain cross-national differences in societal pessimism, while the macro-economic context (economic growth, unemployment) primarily explains longitudinal trends within countries. These findings demonstrate that, to a large extent, societal pessimism cannot be viewed separately from its political and economic context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".