A Return of Trust? Future of Democracy 01.2020 May 2020.
Bibliographic record
Abstract
The initial phase of the corona crisis has led to a significant improvement in the levels of confidence that Germans have in their state and government. More than two-thirds of all people in Germany currently regard the state as being “rather strong” or “very strong.” This means that the level of trust has risen by 23 percentage points since the end of 2019. At the same time, less than a quarter (23%) still think the state is “rather weak” or “very weak.” That is only about half as many people as at the end of 2019. In addition, more than twice as many people (49%) compared to last year, consider our government to be “strong enough,” and only half as many currently view the political system and political stability as weaknesses. Satisfaction with the government has also reached a high level as compared to other countries. Thus, the initial phase of combating the pandemic has led to a massive return of trust in the state’s and the government’s ability to act. The current trust levels are the highest seen in more than twenty years. Although there was still talk at the end of 2019 of an “erosion of trust,” public sentiment has turned completely around during the first phase of the crisis. But how stable are these figures? In any case, one thing is certain: The measured confidence levels are situation-related “performance evaluations.” In other words, they depict sentiments related to an ongoing event. If the assessed event changes, trust levels can also change again. In the process, short-term setbacks are just as imaginable as further consolidation or improvement. Therefore, the measured values represent situation-specific sentiments rather than basic convictions independent of current events. Nevertheless, they do show that the first phase of combating the pandemic has led to a significant increase in popular trust in the government. This freshly gained capital could still be needed in subsequent phases, so it must not be carelessly squandered in the phase of initial easing that is just now beginning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.077 | 0.022 |
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".