Two Orlando stories

As it turned out I wrote two very different stories about Orlando this week. One about the past, one about the recurring present.

For Timeline: In the 70s, Orlando’s gay bars catalyzed a community

It’s a story that deserved to be told better. Which it was to some extent by the memory palace, in a 10-minute tribute to White Horse, the maybe-oldest gay bar in the United States. It’s been around since the thirties.

Listen. A White Horse.

For CJR: The Fundamental dilemma of covering the Orlando shooting

It boils down to this:

But ultimately, with as many answers as we report and confirm and document, one unanswerable question will remain: the question of why a man would pick up a gun and shoot 100 people while they danced.


Now that we know everything

Once, journalists knew some things, but not all things. So they reported on some murders, the ones that raised eyebrows. They wrote about some events around town, and some that happened far away. They wrote most stories about the unordinary and occasionally questioned the ordinary.

Now we know everything. We know every murder in Los Angeles, ever person who’s been killed by police in 2015, every mass shooting in 2015, every mass grave found in Mexico¬†uncovered in the last 10 years and how many bodies in each one. We even know every insult Trump has made on Twitter in the last 7 months.

Sure, there are limits. We know every school shooting in 2015 as Vox defines it. We know only as much detail as we’ve chosen to collect, within the timeframe we’ve chosen. And we sometimes use words that no numbers at all can explain. If a man is black, what makes him black? If a man is unarmed, what makes him unarmed?

Certainly, knowing the tally is not everything. There are other questions. About why it happened and what’s to be done about it, and who were the people before they were in the spreadsheet, and who are they now.

Somehow though, reading the ‘Every Time This Thing Happened, Mapped’ stories feels like an ending instead of a beginning. The definitiveness feels heavy. Like we should take a moment just to recite the names, and that will take up all the time we have.

This isn’t true only of journalists by the way. There are fields now for computational sociology and computational criminology and computational everything else. It’s another way of saying big data. Ocean big data. Space size data. Data data data.

It’s so enticing, this notion of knowing, of capturing the squirming human psyche in a gridlike model. But have we?

Already in 1921 Walter Lippman was lamenting the impossiblities of knowing everything and the weakness of applying the certainty of physics to the haphazard knowing of the social sciences, which sounds remarkably like journalism. He writes,¬†“If you are going to Armageddon, you have to battle for the Lord, but the political scientist is always a little doubtful whether the Lord called him.”

What a wonderful way to describe it.

Maybe as time goes on we’ll find out that with enough data even the human is knowable, but at least for the foreseeable future we’ll continue to elude ourselves.

Losing your center

Decentralization. That’s the theme of the day. We’re not linear, we’re networked. We’ve lost the center, the authority, even the middle-man. Often called democratization, as in the ‘the democratization of news’, it engenders a heady feeling of freedom, access, openness, breaking down barriers.

It’s things like: crowdfunding, peer-to-peer lending, the sharing economy, q+a forums, citizen journalism. On the more technical side: decentralization is the core of bitcoin and its underlying blockchain technology. Also mesh nets, where devices can connect to each other without going through a main server.

Mesh nets remove what could be a choke point. Now, if a government, or a bad actor, wants to shut down the internet, all they have to do is shut down AT&T or Comcast and we all go dark. In a mesh net, every device (that’s configured to use the technology) is connected to every other device within a certain proximity, so you’d have to shut down every connected device separately.

Decentralization is less a positive than simply the reality, with certain characteristics.

First of all, the idealism gets old quickly. Uber is not a power-to-the-people organization, it’s a capitalistic enterprise leveraging new technologies for (surprise!) money, and it has to work within the current framework of government regulation, whether they’ll admit it or not. Second, networks can be super inefficient, and messy, and not great at curating.

Look at journalism. The internet has given a voice and a platform to millions. We have this tremendous clamor of voices saying incredibly wise, mundane, stupid, cruel, quirky things. (In theory, this is wonderful, but the reality of it challenges our democratic notion that everyone SHOULD have a voice. Just read all the jeremiads about how the internet has ruined journalism, and ergo, the world.)

What we no longer have is a Walter Cronkite, or the New York Times (of old) – a central command, respected by the nation as a whole. There is no national conversation, or agenda. We don’t know who to trust. And that’s a loss. Authority is not inherently bad, not when it’s used to guide instead of dictate, to inform instead of command, and when it responds to the will of the people.

All the old media/new media discussions compare then and now like they exist on a spectrum. It’s not clear that they do. There’s a new world-order. The new isn’t on a spectrum, it isn’t linear. There’s a chance it will turn into anarchy or the dictatorship of the mob, and a chance we’ll become a more open, voluble, accessible society where everyone knows how to make the best use of the bewildering amount of choice around him or her.

Data Thursday I

Data Thursdays is a 3-part lecture series given at Columbia University to the students at the Graduate School of Journalism, by Mark Hansen.

I was prepared for a 3 hour lecture about excel sheets and and FOIA requests. Instead Mark Hansen, the data guy at Columbia, gave the most stimulating talk I’ve heard in a while — about the world of data that we live in.

1. Data is a model
One of Hansen’s slides had this formula “digital technology = model of world = argument”. Every technology we use is operating on a model of the world and then “acts as a relentless argument for that model”.

Google for example digitized the world in one way, and has been training us to use its model for a decade (whether in good faith or not is irrelevant). As citizens, consumers, users, we often accept technology as is, but as journalists, we have to question the model. How does it work this way? Why does it work this way? What assumptions are being made? What sacrifices are being made in order to accommodate this model?

2. Algorithms/Data are not objective
If technologies and algorithms exist as models, not facts, then they are proxies created by humans, and it therefore follows that they are subject to all the human error and bias of their creators. While working on a project for the 9/11 museum, he was asked to create a display using an algorithm, so that it would be objective. “An algorithm is not objective,” Hansen said emphatically. “It may be systematic, but it’s not objective.”

3. Creativity is where you “turn the world to bits”
In the lecture, Hansen went through many things (material and conceptual) that we encounter in everyday life and asked how we would turn them into data – time, money, a bodega, a movie. It was to make us begin to think about that space where a house on a city street becomes a value that can be added and subtracted — where the world becomes ‘bits’. That’s where the creativity is. And probably that’s where the danger is. How reductive are we willing to be?

4. Data is an exchange
Data means given, as opposed to capta, which means taken. If data is something you’re given, it is not a one-way transaction. You have a responsibility to the givers – and that doesn’t necessarily mean the compilers, it could mean the values that make up the data – the families in the census, the users who click on this or that ad etc.

Doesn’t this change the conversation?

tl;dr The job of a data journalist is to question the algorithm. Question the model. (Not to drown in excel sheet.)