Bibliographic record
Abstract
This paper wants to verify how politicians move within different communication channels, both in traditional networks (i.e., exploitative communications) and social network (i.e., explorational communications). The study of the role of these two forms of communication may ameliorate the current understanding of how politicians communicate with citizens; and also it aims at examining the relationship between traditional forms of communication used by politicians (here intended as the use of a personal website) and the use of social network (here measured in terms of the number of posts on Twitter) in influencing the tendency to be followed on social networks.This research aims to demonstrate that the choice of politicians in adopting both exploitative communication and explorational communication is more effective and efficient than choosing to adopt a single strategy.It is made two different study. In the study 1, we a collected data about votes by section, number elected by section, average voters for the last legislature for each Italian party. Data were extracted from the Minister of Internal Affairs. Five models were created and they are estimated for the most important Parties of the last legislature in which reliable data exist for votes by section, number elected by section, average voters, Followers on Facebook, Likes Instagram, Likes Facebook, Followers on Instagram, Facebook Profile and Followers on Twitter.The supplementary analysis is further test of our hypothesis. We a collected data concerning traditional channels - in particular the use of institutional websites and personal websites - and data relating to social media in particular, were collected for each individual parliamentarian (deputies and senators).Specifically, a moderation analysis was conducted in which followers on Twitter served as the dependent variable. The results support our hypothesis.This study has found important and significant results compared to the use of social media by Italian politicians. However, it has many potentialities to explore.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".