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

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RSS r-bloggers.com

  • Angela Bassa discusses managing data science teams and much more.
    Hugo Bowne-Anderson, the host of DataFramed, the DataCamp podcast, recently interviewed Angela Bassa, the Director of Data Science at iRobot. Here is the podcast link. Introducing Angela Bassa Hugo: Hi there Angela, and welcome to DataFramed. Angela: Thanks, thanks for having me. Hugo: It's a great pleasure to have you on the show, and I'm […]
  • Preview my new book: Introduction to Reproducible Science in R
    I’m pleased to share Part I of my new book “Introduction to Reproducible Science in R“. The purpose of this …Continue reading →
  • How to de-Bias Standard Deviation Estimates
    This note is about attempting to remove the bias brought in by using sample standard deviation estimates to estimate an unknown true standard deviation of a population. We establish there is a bias, concentrate on why it is not important to remove it for reasonable sized samples, and (despite that) give a very complete bias […]
  • Data Science With R Course Series – Week 9
    There are only two more weeks in the course! This week will extend what you learned from the Expected Value by performing an optimization and sensitivity analysis. The optimization and sensitivity analysis will teach you how to identify the maximum bu...
  • RATest. A Randomization Tests package is available on CRAN
    This blog post introduces the RATest package we released a while back on CRAN with my colleague and good friend Mauricio Olivares-Gonzalez. The package contains a collection of randomization tests, data sets and examples. The current version focuses on two testing problems and their implementation in empirical work, mostly related to economics. First, it facilitates […]

RSS Simply Statistics

  • The role of academia in data science education
    I was recently asked to moderate an academic panel on the role of universities in training the data science workforce. I preceded each question with opinionated introductions which I have fused into this blog post. These are weakly held opinions so please consider commenting if you disagree with anything. To discuss data science education we […]
  • Guest Post: Galin Jones on criteria for promotion and tenture in (bio)statistics departments
    Editor’s Note: I attended an ASA Chair’s meeting and spoke about ways we could support junior faculty in data science. After giving my talk Galin Jones, Professor and Director of Statistics at University of Minnesota, and I had an interesting conversation about how they had changed their promotion criteria in response to a faculty candidate […]
  • The economic consequences of MOOCs
    tl;dr check out our new paper on the relationship between MOOC completion and economic outcomes! Last Monday we launched our Chromebook Data Science Program so that anyone with an internet connection, a web browser, and the ability to read and follow instructions could become a data scientist. Why did we launch another MOOC program? Aren’t […]

RSS Statistical Modeling, Causal Inference, and Social Science