Cosa possiamo imparare dal M5S

Leggo e rispondo al post di Massimo Mantellini (Il M5S, il wifi e il principio di precauzione) in cui si evidenzia con preoccupazione come il Movimento abbia portato in Parlamento, dunque in qualche modo legittimandole, posizioni anti-scientifiche; un “pensiero tossico, banale e a suo modo inattaccabile, che nuoce al Paese intero”.

Il Movimento Cinque Stelle con un bacino elettorale che si aggira tra il 25 e il 30% (8.5-10 milioni di persone) è necessariamente complesso in termini di rappresentanza demografica e di diversità di opinione. Considerando un astensionismo del 25%, se vi trovate in fila al supermercato delle 10 persone che vi precedono circa due votano M5S. Purtroppo questa complessità raramente traspare nelle narrazioni giornalistiche, e chi fa informazione tende (troppo) spesso a preferire i tratti caricaturali (da cappello di carta stagnola o da gita in Corea del Nord, per intenderci). Ma questo tipo di informazione è sbagliata: primo perché distorce nella semplificazione, secondo perché incoraggia comportamenti macchiettistici, grotteschi e sbracati da parte di chi sedendo in istituzioni affollate cerca visibilità.

(more…)

Friday, 22 July 2016

Road to Rome: The organisational and political success of the M5S

The Five Star Movement (M5S) obtained two major victories in the second round of municipal elections on 19 June 2016 in Rome and Turin. Rome attracted the most international attention but it is M5S’ victory in Turin that is likely the most consequential for them and other European anti-establishment parties.

In Rome, a municipality with 2.8 million people and an annual budget of €5 billon, Virginia Raggi (age 37) gained doubled the votes of her contender Roberto Giachetti (age 55). In Turin, a city with a population of 900,000 and an annual budget of €1.69 billion, Chiara Appendino (age 31) outstripped Piero Fassino (age 66) by about 10 percentage points.

Continue reading on Pop Politics Aus

Friday, 8 July 2016

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • RObservations #31: Using the magick and tesseract packages to examine asterisks within the Noam Elimelech
    Introduction Since my last blog on Tesseract-OCR I have been playing around casually with it to see what it is possible of doing. Tesseract supports optical character recognition for over 100 languages. That together with straight forward usage for implementing it in R inspired me to try using it for Hebrew ... Continue reading: RObservations […]
  • How to add labels at the end of each line in ggplot2?
    The post How to add labels at the end of each line in ggplot2? appeared first on Data Science Tutorials How to add labels at the end of each line in ggplot2?, Using the ggplot2 R library, this article shows how to display the last value of each line as ... Continue reading: How to […]
  • Top 3 Tools to Monitor User Adoption in R Shiny
    Can you monitor user adoption for R Shiny apps? What is user adoption anyway? We’ll answer these questions and show you how to do it yourself in this article. Put simply, user adoption is the process by which new users become familiar with your product and/or service and ... Continue reading: Top 3 Tools to […]
  • Artificial Intelligence Examples-Quick View
    The post Artificial Intelligence Examples-Quick View appeared first on Data Science Tutorials - Are you curious about Artificial Intelligence Examples? If you answered yes, then this article is for you.  We’ll go over some Artificial Intelligence instances here. So, spend a few minutes reading this article to learn everything ... Continue reading: Artificial Intelligence Examples-Quick […]
  • Bayesian sampling without tears
    Following a question on Stack Overflow trying to replicate a figure from the paper written by Alan Gelfand and Adrian Smith (1990) for The American Statistician, Bayesian sampling without tears, which precedes their historical MCMC papers, I looked at the R code produced by the OP and could not spot an ... Continue reading: Bayesian […]

RSS Simply Statistics

RSS Statistical Modeling, Causal Inference, and Social Science

  • “Stylized Facts in the Social Sciences”
    Sociologist Daniel Hirschman writes: Stylized facts are empirical regularities in search of theoretical, causal explanations. Stylized facts are both positive claims (about what is in the world) and normative claims (about what merits scholarly attention). Much of canonical social science … Continue reading →
  • New Yorker : Spy :: Kieran Healy : Statistical Modeling, Causal Inference, and Social Science
    Back in the day, the New Yorker magazine had an Olympian attitude and did not run letters. Spy magazine rectified this with a column, Letters to the Editor of the New Yorker. The New Yorker now runs letters, but Kieran … Continue reading →
  • Webinar: Design of Statistical Modeling Software
    This post is by Eric. On Wednesday, Juho Timonen from Aalto University is stopping by to tell us about his work. You can register here. Abstract Juho will present what he thinks is an ideal modular design for statistical modeling … Continue reading →