Do you remember how clear my Orsai graphs were?
Now, I call it the Orsai mess.
But before you see it, here’s an introduction of what happened.
The graph above includes only the ‘input’ from authors and editors, and is organized according to what medium they were published on (Orsai Blog, Bar Blog, Redacción Blog, Web Magazine, and Print Magazine). This is, so to speak, the text, the narrative of the project and, as any narrative, it relies on a million different strategies aimed at producing a reading effect. Well, in the case of Orsai, it is also a buying effect, otherwise all kinds of bad things happen: authors don’t get paid, magazines don’t get delivered, people get angry and, in short, the project dies.
Well, all of that, as it can be read in the narrative (the graph) is clearly orchestrated and maintained. It is a well-oiled machine.
Then come the readers.
Studying the figure of the reader has always been messy. It has also always been my thing. From the earlier posts here, you’ll see that I’m interested in what happens when we read, on what happens when others read the same. Well, what happens when 5 thousand readers read the same? This happens:
This is what I *love* about the possibilities of reading in participatory media platforms. There are traces of people’s readings. And what I *love* about my project: I can study those traces, I can study ‘readings’. So in this case, the original dataset was sliced to leave only those ‘pieces’ with a commenting section enabled – only the print magazine pieces were taken out really. The figure of the reader is considered not someone who reads Orsai, but someone who comments on Orsai.
Now, from whatever’s going on the the Orsai mess, for now I just want to mention that if we put together each article, blog post, chronicle, short story, and reader comment as one item, readers have produced 99% of that. This is only a +2 year project, and it amazes me how much it is both dwarfed and impossibly expanded by those who read it.
Imagine the possibilities!
*As always, many thanks to versae for all his help. And special thanks to Hernán Casciari for the huge dataset he so generously gave me.