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.

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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

  • Backcast a Time Series for Covid-19 Truths
    A couple of months ago, Turkey’s Health Minister announced that the positive cases showing no signs of illness were not included in the statistics. This statement made an earthquake effect in Turkey, and unfortunately, the articles about covid-19 I have wrote before came to nothing. The reason for this ... The post Backcast a Time […]
  • The Impact of the COVID-19 Pandemic on My Walking Behavior in 2020
    In this post, we will take a look back at 2020, and analyze my step count data to understand some of the impacts that the COVID-19 crisis had on my walking behavior during that crazy year. The Data Step Counts & Measurement Devices The step count data come from 2 sources in 2020 - ... […]
  • Share R shiny apps with brightRserver: 70-second sneak-peek
    Building, maintaining, and improving interactive R web apps has never been easier. YakData’s brightRserver seamlessly combines the best-in-class R editor and R web app server with Secure FTP publishing and synchronization. The post Share R shiny apps with brightRserver: 70-second sneak-peek first appeared on R-bloggers.
  • Making a Solar Insolation Map for Alberta (For novices!)
    Been a while since I've blogged here; wrapping up an MSc and moving continents from Europe to North America is all the excuse I need. This blog post is not going to be revolutionary, and obviously it builds on a lot of what others have done before (see... The post Making a Solar Insolation Map […]
  • Counting Missing Values (NA) in R
    To check for missing values in R you might be tempted to use the equality operator == with your vector on one side and NA on the other. Don’t! If you insist, you’ll get a useless results. x The post Counting Missing Values (NA) in R first appeared on R-bloggers.

RSS Simply Statistics

  • The Four Jobs of the Data Scientist
    In 2019 I wrote a post about The Tentpoles of Data Science that tried to distill the key skills of the data scientist. In the post I wrote: When I ask myself the question “What is data science?” I tend to think of the following five components. Data science is (1) the application of design […]
  • Palantir Shows Its Cards
    File this under long-term followup, but just about four years ago I wrote about Palantir, the previously secretive but now soon to be public data science company, and how its valuation was a commentary on the value of data science more generally. Well, just recently Palantir filed to go public and therefore submitted a registration […]
  • Asymptotics of Reproducibility
    Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, in part, there are so many good tools out there! This is undeniably a good thing and […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Hierarchical stacking
    (This post is by Yuling) Gregor Pirš, Aki, Andrew, and I wrote: Stacking is a widely used model averaging technique that yields asymptotically optimal predictions among linear averages. We show that stacking is most effective when the model predictive performance is heterogeneous in inputs, so that we can further improve the stacked mixture by a […]
  • The norm of entertainment
    Someone pointed me to a comment that a psychology researcher wrote that he almost never reads our blog and that it “too quickly bores me.” That’s ok. I’m sure that lots of people have stumbled upon our blog, one way or another, and have been bored by it. We don’t have a niche audience, exactly; […]
  • Tessa Hadley on John Updike
    Lots to think about here. To start with, this is the first New Yorker fiction podcast I’ve heard where they actually criticize the author instead of just celebrating him and saying how perfect the story is. This time, they went right at it, with the interviewer, Deborah Treisman, passing along some criticisms of Updike and […]