What a Ted Cruz Super PAC ad got wrong about the Supreme Court

I’ve been doing some work for the Internet Archive on a project called Political TV Ad Archive. Yesterday I was watching some of the ads and came across one from Courageous Conservatives, a Ted Cruz-friendly Super PAC whose money comes from the deep pockets of a Missouri construction figure.  

This spot was published on Feb. 4. The narrator notes that, “With four Supreme Court justices over the age of 80 who New Hampshire chooses has never been more important.”

It ran on Youtube nine days before Supreme Court Justice Antonin Scalia was found dead on Feb. 13.

But Scalia was only 79 when he died and he was the second oldest justice on the bench. Ruth Bader Ginsburg is the oldest. She was born on March 15, 1933. None of the other justices are 80.

So how did Courageous Conservatives come up with the count of four over 80?


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Five negative ads with big air time in New Hampshire

As the New Hampshire primaries approach, here are the five negative ads getting the most airplay over the past week in the Granite State, according to Political TV Ad Archive data, a project I have been doing some work for. It’s connected to the Internet Archive, which has loose ties to Aaron Schwartz. That was enough for me to jump on board.

1. Marco Rubio campaign ad focusing on America, while identifying Hillary Clinton as opponent.

In this ad sponsored by Marco Rubio’s campaign fund, the candidate talks of patriotism and defines the race as being about “defeating Hillary Clinton,” without mention of any of his GOP opponents.

Top Wall Street firms, such as Goldman Sachs, Morgan Stanley, and the Blackstone Group, are high among the top contributors, via employees and PACs, to Rubio’s campaign fund, according to the Center for Responsive Politics. Wall Street firms also figure prominently among Hillary Clinton’s campaign donors. For more detail, click here.

2. Donald Trump campaign ad criticizing Ted Cruz on his record on immigration. 

Trump focuses on Ted Cruz and his immigration record in this ad. Immigration has been a large focus of GOP ads in the primary. To see immigration statements fact checked by PolitiFact, click here.

3. Conservative Solutions PAC, super PAC supporting Rubio, criticizing Clinton and Jeb Bush. 

The super PAC supporting Marco Rubio clams that Clinton and Bush are from the past in this ad, and puts forth Rubio as a candidate for the future. The top donor to this super PAC, according to the Center for Responsive Politics, is Braman Motors, with $6 million. Norman Braman, the auto dealer, reported Politico, himself has a grudge from the past: a 2004 veto by former Florida Governor Jeb Bush against a charity he favors, which Rubio, the New York Times reported, championed.

4. America Leads, super PAC supporting Chris Christie, calling out John Kasich for being close to the banking industry.

This ad hits Kasich hard for being close to the banking industry. To see the donor list for the super PAC from the Center for Responsive Politics, click here. The super PAC raised $16 million last year, reports the Center for Public Integrity, and has opted for a reporting schedule that lets it delay details of its January donors until  “well after the first-in-the-nation Iowa caucuses and New Hampshire primaries are conducted.”

5. American Future Fund criticizes Kasich on tax record

In this ad, the nonprofit American Future Fund, which is not required to disclose its donors, but which the Center for Public Integrity reports has ties to the conservative Koch brothers network, attacks Kasich on his tax record as governor of Ohio. Factcheck.org writes that the ad tells “only half the story,” that the ad “ignores that while Kasich in early 2015 proposed billions in tax increases — mostly from sales and cigarette tax hikes — the very same plan proposed an income tax cut and other tax relief. Overall, the plan called for a net tax cut.” The Washington Post’s Fact Checker awards the ad “three Pinocchios” because “Even Kasich’s critics agree there has been a net tax decrease in Ohio under Kasich.”

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What is code anyway?

Good question, actually. The answer from Paul Ford — “What is Code?”– is tearing through the journo tech listservs and FB groups. So many pretty colors and it’s fun!

Here’s my favorite part:

Why Are We Here?

We are here because the editor of this magazine asked me, “Can you tell me what code is?”

“No,” I said. “First of all, I’m not good at the math. I’m a programmer, yes, but I’m an East Coast programmer, not one of these serious platform people from the Bay Area.”




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Growing pains in Python

Sometimes I feel like I am in a jungle with a dull machete and no compass. It’s 110 degrees, I’m sweating and trying not to panic. The feeling is what Erik Trautman would probably classify as “the Cliff of Confusion” in his funny, somewhat reassuring, somewhat unsettling piece: Why learning to code is so damn hard.  One woman’s jungle is another person’s cliff. Same sinking feeling. Either way, the feeling is normal. You just have to get through the desert of despair and you will find the Upswing of Awesome.

To summarize: Don’t try this s*$! alone. Get a mentor and find someone you can sit with or skype with or whatever when you have questions.

The boxing ring has taught me that it might hurt to get hit in the nose (my ego more than my nose usually) but that the only recourse is to keep my composure (it’s normal to get frustrated when you get hit or get stuck in Python), dig in deeper and get back into the ring.

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Python and the search for cuberoot and other adventures

I am about a month into the Data Science in Python course. The Python is okay, the data science is okay. Yes, even the linear regression turned out to be okay.

But learning on top of everything else the ins and outs of Pandas, lamdas, and all kinds of other modules and helpers is killing me slowly. Actually it happened pretty fast. A lot of machine problems too. So much learning!

I have to say that the Python tutorials on Code Academy redeemed my opinion of the site, which was, I admit, based on JavaScript tutorials circa 2012. The Python I learned in the past (Learn Python the Hard Way!) and this time are coming together. But it’s still hard for me to work the other way around. You know, like when you learn another language and feel confident but then translate from your native language into the foreign one? God it would great if people just walked around speaking Python instead of English or Spanish. I’d pick it up really fast!

Instead things are slowly coming together after an intense review. I’ve always been a good troubleshooter. Now I’m so good at it I have an inside joke that I should rename this blog “Finding the Cuberoot.”


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Changing the world with data journalism

In January 2014, Kenyan broadcaster NTV aired a 12-minute, data-driven video about the impact of drought in Turkana, an impoverished, isolated region of northern Kenya.

The piece combined personal stories with government data to show the impact of intense and frequent drought-related famines on childhood malnutrition. The story had a measurable impact. 

However, as Internews President Jeanne Bourgault wrote from the World Economic Forum in Davos: “Open data has the potential to drive positive social change, and data-journalists can be key to creating the stories that bring that data to life. But getting there is not easy.”

It’s a thoughtful and unusually (for journalism) level-headed discussion about data journalism.

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Thinkful-Girl Develop It Data Science in Python scholarship!!!

It happened! I received a ThinkfulGirl Develop It scholarship for a Data Science in Python course. So things are about to get very busy until mid-May. But this is the kind of chance I’ve been waiting for to get past just scratching the surface of data journalism.


I’ve wanted to dig deeper. But there has been a big thick wall standing in the way of me and anything more complicated than Excel — even though I am no stranger to computers, apps and coding. I taught myself HTML years ago (when we were called “webmasters”). I even started a JavaScript study group a few years ago at Sudo Room in Oakland. I also teach data journalism techniques and founded a civic app project, Civic Playground, which produced several prize-winning apps. I teach all kinds of digital journalism skills at San Francisco State University. I do a monthly data-driven column for a magazine and rely on all sorts of datasets for my regular and investigative reporting.

But that wall has not budged, at least not with just me pushing on it. Now, beginning on Wednesday, I’ll be tackling SQLite, APIs and scraping, as well as wrestling with probability, hypothesis testing and linear regression.

I will admit that just the words linear regression freak me out a little bit. But I got a note from my mentor and she (SHE!) is doing exactly what I’m shooting for. And there is no getting around the statistics side.

I have a few goals, some practical and short-term. The first  is to write a scraper that will allow me to compare the number of stories written by men versus the number by women about the firing of former New York Times editor Jill Abramson. I want to learn things like how to write an API that will scrape lawsuits filed in county courts and automatically feed into a Google spreadsheet (this is entirely legal and done by other journalists). And I want to expand my knowledge of Python. The bigger goal is data mining — finding those patterns that would otherwise be hidden. That is what I want for my reporting and what I want to show other women, including my daughters in college, they can do.   

In case you are wondering why a journalist wants to study data science, reporters are waking up to the potential of public data as more and more are being produced and put online. A subset of reporters have been using technology for their work since the practice was called computer assisted reporting and Excel, SASS and MySQL were the tool of choice. Now data journalism — which CAR has branched into — is becoming way more sophisticated as reporters enter the realm of data science. I think it’s going to make investigative reporting even richer.

I’m not expecting to emerge in three months as a full-fledged data science butterfly although I will certainly be working to get as far out of the chrysalis as possible. But the Thinkful course is a start that includes a real foundation. So Naive Bayes and cluster analysis, here I come!  


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Where data and multimedia collide

I started teaching multimedia journalism and digital news gathering at San Francisco State University in late August. I won’t tell you all the lessons I have learned and weaknesses revealed to me and others in the past two months. No use dwelling on these things: fix what can be controlled and do your best to remedy the rest.

The classes are a return for me to the backpack, MOJO days. I started as a reporter with a camera and switched to writing (no use calling it “print” journalism anymore) because pen and paper were easier for me. I’m not an artist and fail at lighting to the degree that my best advice usually boils down to don’t shoot btw noon-2 pm, turn off the ugly lamps and put your person near a window in low-light situations. However, I love shooting video and audio and love even more editing it.

But I keep having to pull myself back from treading straight into data journalism because  infographics/data visualization don’t seem to be counted as multimedia. I’m just still trying to pin down exactly what multimedia is — or could be — now.

It’s defined often as video, audio and photo + text. Maybe transmedia and interactive games. But for most people it’s the latter.data-visualisation1

So I wrote this post on the class blog I keep (it mirrors blogs the students in my classes keep as well at my insistence). I’m planning to teach them video animation, JSTimeline, Meograph, simple Google Fusion maps and we’ll build a Bootstrap site or use a tarbell template.

Why do the two worlds of data and multimedia feel separate, and isolated? Why don’t I hear much cross-platform discussions? Maybe I have to listen closer.


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Migrahack at CSUN

Just got back last night from the Migrahack at Cal State Northridge. I’ll post photos and more but wanted to get the links up here from my presentations. I got a chance to sit in on Ron Campbell’s Census workshop because I finished mine early. Campbell was one of my early data journalism heroes. Sure enough, in  just 5 minutes of his workshop I walked away with IPUMS and Census Reporter. The NAHJ student journalists gave me inspiration and a shot of determination. Anyway, I’ll be updating my presentations in the next few weeks with more Tableau, BLS, Census Reporter and IPUMS. But I’ll post the information here when that happens.



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Los Angeles County is full of Germans…or?

I’m here somewhere near Burbank getting ready for Migrahack at Cal State University at Northridge, or, as I have heard people here call it, CSUN (as in see-sun).  Anyway, I’ve been playing around with some Census figures – the amount of time men and women spend commuting to work or how early men compared to women leave home for work every day. I stumbled across the ancestry table. Who knew there were so many people in L.A. County who counted German ancestry: 495,046. Not a lot when you consider there are nearly 10 million people here.  But it’s No. 1. EXCEPT for the 7 million people shoved into “other groups.” You tell me which group is missing in the table below. I am not sure why the Census puts the information together like this. It’s  an example for the class of what you can find on the Census, or in this case what’s missing sometimes.

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