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#datascience

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Miguel Afonso Caetano

"This paper advances the critical analysis of machine learning by placing it in direct relation with actuarial science as a way to further draw out their shared epistemic politics. The social studies of machine learning—along with work focused on other broad forms of algorithmic assessment, prediction, and scoring—tends to emphasize features of these systems that are decidedly actuarial in nature, and even deeply actuarial in origin. Yet, those technologies are almost never framed as actuarial and then fleshed out in that context or with that connection. Through discussions of the production of ground truth and politics of risk governance, I zero in on the bedrock relations of power-value-knowledge that are fundamental to, and constructed by, these technosciences and their regimes of authority and veracity in society. Analyzing both machine learning and actuarial science in the same frame gives us a unique vantage for understanding and grounding these technologies of governance. I conclude this theoretical analysis by arguing that contrary to their careful public performances of mechanical objectivity these technosciences are postmodern in their practices and politics."

journals.sagepub.com/doi/10.11

#DataScience#STS#Insurance
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☮ ♥ ♬ 🧑‍💻

Day 19 cont 🙏⛪️🕍🕌⛩️🛕 💽🧑‍💻

“The #LiberalParty has accidentally left part of its email provider’s #subscriber details exposed, revealing the types of #data harvested by the party during the #election campaign.

This gives rare #insight into some of the specific kinds of data the party is keeping on voters, including whether they are “predicted Chinese”, “predicted Jewish”, a “strong Liberal” and other #PersonalInformation.”

#AusPol / #DataScience / #inference / #voters / #Liberal / #LNP / #Nationals <crikey.com.au/2025/04/17/victo>

Crikey · ‘Predicted Chinese’, ‘predicted Jewish’: Liberals accidentally leave voter-tracking data exposedBy Cam Wilson
Public
R-Podcast (Eric) :pci:

Episode 202 of the @rstats @rweekly Highlights Podcast is out! serve.podhome.fm/episodepage/r

🚀 R 4.5.0 has landed (Russ Hyde) @jumpingrivers
🚰 plumber2 @thomasp85
🗒 Introducing chores @simonpcouch.com @posit.co

What has you excited about R 4.5.0? Let us know! And if you're enjoying the podcast please share with your friends and network.

h/t @mike_thomas 🙏 (and that Nix-loving curator)

R Weekly HighlightsIssue 2025-W16 HighlightsIt's not every day that we get to dive into a brand new release of R, but we get to do just that in episode 202! We share our takes on the major new…
#RStats#DataScience#V4V
Public
Bjørnar (he/him)

If you work in the #r world, don't make your sample ids integers. Someone is going to accept the type from the read function in one part of their package and force the type to be a character in a different part and then you'll get an error from the step where they're merged or compared.

"Fortunately" in my current project I already have to copy and paste the code for the package from GitHub for other reasons anyway, so I can put in a fix... (Stupid AllOfUs.)

#datascience
Public
Miki :rstats:

#30DayChartChallenge Día 11: Stripes! Mi versión: ¡El código de barras del pánico del mercado! 😱

Este gráfico muestra una línea de tiempo (1993-2025) donde cada raya vertical representa un día en que el VIX cerró ≥ 30 (¡alta tensión!).

El concepto clave aquí es el **Volatility Clustering**: la alta volatilidad no se distribuye uniformemente, ¡viene en rachas! Los densos grupos de rayas identifican visualmente las grandes crisis (Dot-com, GFC '08, Covid '20...). Los largos periodos en blanco son la calma relativa.

Es una forma directa de ver la *persistencia* y los *regímenes* de la volatilidad del mercado. ¡Olvida las medias simples, el estrés viene en oleadas! 🌊

🛠️ Hecho con #rstats, #ggplot2, #quantmod.
📂 Código/Repo: t.ly/-vd9u

Gráfico estilo código de barras en una línea de tiempo de 1993 a 2025, visualizando días de alta volatilidad del mercado. El eje X muestra los años. Rayas verticales finas de color morado marcan cada día en que el índice VIX cerró igual o superior a 30. Se observan densos clústeres de rayas alrededor de periodos de crisis como 2001-2002, 2008-2009 y 2020. El eje Y está oculto. Título: "Días de Alta Volatilidad del Mercado (VIX >= 30)". Fuente: Yahoo Finance.
#Day11#Stripes#dataviz
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