impact of the pandemic on communication between local government and citizens in a small village in Tuscany
Bibliographic record
Abstract
The COVID-19 pandemic has fostered the increasing use of digitally-mediated communication, which has substituted a large part of the face-to-face encounters, work, political, social, and leisure activities, made impossible during the long period of lockdown. What did this entail in small villages, in respect to both citizens and local government, where face-to-face communication has been more resistant to digital mediation? This study aimed to explore the changes seen in institutional communication, and more generally, in the everyday life of citizens and their relationship with local administrators during the first lockdown in Italy. The context explored was the small-scale local community of Peccioli (Tuscany), a village where face-to face communication usually played a pivotal role in the interaction between local government and citizens. This small village represents a good point of observation to understand whether, in contexts such as this, there has been a change in the balance between different modes of communication similar to that seen in more urban environments. More specifically, the paper presents the main findings emerging from a study exploring on the one hand, the attitudes and opinions of local administrators regarding institutional communication, and, on the other, the evaluations by citizens of the initiatives and the communication by local government and an analysis of their information behaviors. In the first case, a qualitative approach was used, based on 10 semi-structured interviews with local administrators; in the second case, a quantitative approach was adopted based on a survey conducted with a representative sample of Peccioli’s citizens. The main finding of the study revealed the crucial role of word of mouth, thus indicating that, contrary to what is generally believed, not all communication has become automatically digital during COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".