Today’s newsletter! (Want to subscribe? Click here.)
I picked a random piece from the NPR archives. Let’s think of all of the different ways we could categorize this piece.
That’s all I got. What am I missing?
Mel
Update
From Bryant Welch:
Host: Rachel Martin Series: American Made: The New Manufacturing Landscape (http://www.npr.org/series/351733364/american-made-the-new-manufacturing-landscape) Photographer: Ross Mantle Season: Fall 2014 Geographic reference: Ohio River Geographic reference: Japan Geographic reference: Mexico Geographic reference: Boston, MA
From Ted Coltman
Very interesting exercise. This may be too much detail, but two additional aspects we could categorize are the business economics embedded in this piece and more info about the illustrations accompanying the piece on its archive webpage:
Subject: ‘retro’ revival of consumer demand for formerly obsolete products Subject: manufacturing products for dual retail and institutional marketing channels Subject: manufacturing products for multiple countercyclical marketing channels Illustration: 14 JPEG still photos and 2 animated GIF photos Illustration: 2 animated GIFs of production machinery, with products moving along production line Illustration: still photos of 2 male and 2 female employees, other members of whose families have worked for same company etc.
From Sonya Mann
Companies 100+ years old. Americana. Crafts. Generational work.
From Gabe Isman
When I see this list the part of my brain that has done data modeling starts complaining loudly. “Melody, you shouldn’t be trying to make the biggest list, you should make the smallest list that doesn’t lose anything!”
So to my mind, 4 and 5 are totally redundant, that the piece is 7:22 should be enough. Likewise 5 and 6, 9 and 35, etc.
I know these are really something like ‘lists this piece could be long to’ and not ‘data about this piece’, so my data modeling brain should just shut up. But I think something interesting is highlighted by picking out which of these categories is somehow essential, i.e. couldn’t be deleted without loss of information. Some surprisingly obscure bits become essential, for example:
Not surprisingly, I suppose, these are also things that are hard for a computer to figure out on its own. These bits of data are somehow more human than some of the others.
From Kate Myers
First – fantastic piece! You know this, but your newsletter is a really nice bite of insight. I admire you for doing it.
My 2 cents to add - Cult icon, something that people collect.
Then you can take a step to communities that would engage with this – those collectors, people who are interested in the backstory of classic americana, etc.
If we were going to recast all these data points from the perspective of the audience, what would they be? You’re on to something with the “when to listen”, but I wonder if there are more.
From Andrew Losowsky
Decibel rating Waveform Show producer Times broadcast Stations on which it was broadcast
And your own listener data: how you found it, when you found it, had you listened to it before, did you listen to it all, what rating do you give it, who if anyone would you tag it with to share.
From Daniel Newman
The ‘Categorization’, ‘Contains’, & ‘When to Listen’ pieces seem most useful, but also the hardest to systematize. Figuring out how to represent the ‘shades of evergreen’ would be one of the more useful things, if that could be done.
From Sue Diaz
Is ‘subject’ a fixed taxonomy? I would add the woman-led biz aspect, maybe in ‘contains.’
From Josh Tong
Great list! Have you seen the Dublin Core metadata terms? http://goo.gl/sK6oQy Could include format (audio, text) and rights.