I’m hoping to get some direction in class on two ideas, since I have unwisely spent time becoming invested in both.
Idea #1
What if we tried selling all points of a supply chain as much as the end product?
- Uncover as much specific information for the supply chains of as many rare earths, or heavy metals, or depleted elements as is feasible
- Collect language from advertising
- Create ad-like descriptions of starting, intermediary, and end points the supply chain
Concerns:
- A lot of research
- Highlighting bad things can be informative but I find this somewhat problematic when people aren’t empowered to address the issue. Perhaps I’m promoting awareness that reduces consumption?
- As a positive, it could be interesting to include some but I don’t necessarily want to promote of these ‘responsibly sourced‘ product lines as a solution.
Progress
My poem from week 2 was sort of a first experiment with this, where I tried putting the rare earth element back into the end product.
There are also several places to look for supply chain information, although to get a complete picture often takes people several weeks and many databases have paywalls:
- MIT’s Observatory of Economic Complexity
- Source Map
- Rare Earth Elements: The Global Supply Chain – from the Congressional Research Service (2015)
- Conference presentation on the Rare Earth Supply Chain (2014)
IDEA #2
Site-specific found poem
- Sniff for wifi at a specific place (this would require going to the place, so it’s a physical limitation)
- Pull all the tweets from that place (perhaps there’s other geo-located social media that would be fun to incorporate)
- Create poem
I like that it’s could be found poetry that makes a connection to how the words might have been released to the world.
Concerns
- Is twitter poetry Two Thousand and Late?
- Enough twitter material?
Progress
I’m playing with the Twython library and twitter API (link to geosearch doc) but have been having trouble getting the actual tweets by querying geolocation. I know that only about 1% of tweets are geolocated so this might be a fatal limitation. Or maybe my code is just incomplete.
I haven’t delved into this first step of collecting wifi networks.
returned:
{
u ‘search_metadata’: {
u ‘count’: 100, u ‘completed_in’: 0.025, u ‘max_id_str’: u ‘852568659604234240’, u ‘since_id_str’: u ‘0’, u ‘refresh_url’: u ‘?since_id=852568659604234240&q=&geocode=40.72%2C%20-73.95%2C%2010mi&include_entities=1’, u ‘since_id’: 0, u ‘query’: u ”, u ‘max_id’: 852568659604234240
}, u ‘statuses’: []
}