Instituutioihin kohdistuvan luottamuksen ja koronapandemiaan liittyvien rajoitustoimenpiteiden dynamiikat Suomessa
Bibliographic record
Abstract
Dynamics between trust in institutions and restrictions related to the COVID-19 pandemic in Finland Governance of the COVID-19 pandemic is based on the premise that as many citizens as possible follow guidelines and restrictions set by authorities. Successful implementation of these has depended on citizens’ trust. This study examines the dynamics between changes in citizens’ trust in institutions, changes in COVID-19 related restrictions, and changes in the number of COVID-19 cases in Finland between May 2020 and January 2022 using a visual timeline. Changes in citizens’ trust in institutions are evident between surveys, but no clear link between changes in trust and restrictions is evident. Measuring citizen trust during crises is important, as changes can provide governments with information on citizens’ views of the legitimacy and success of crisis leadership, governance, and ultimately, the political system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".