Cara Giaimo
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Too-Clean Data

August 1, 2010 @ 9:54 PM | Permalink

It had started pouring at an inopportune time, and the mud was hindering our progress. My classmates and I were scattered in clumps of two or three across a wide transect delineated by our Environmental Policy professor. We were supposed to be interviewing the people who lived here about their experiences with human-wildlife conflict; it was an exercise I repeated at least fifteen times over my three months in East Africa, first for class and then for my own research. This was, apparently, going to be a slightly different experience than the others, a Variation in Knee-Deep Mud. We squelched along. Our guide, Moses, was leading us to our third house of the day, and to get there we had to make it through the attached farm, a maze of wet plants sticking their heads out of the ground."Pole pole!" Moses said cheerfully, easily placing his steps correctly without looking. Slowly, slowly. It's hard enough for a cabbage to make it above ground in Africa, and once it does, everything’s out to get it.  I didn’t want any to die by my hand, or, more likely, my hiking boot, which the mud was turning into more of a platform shoe.  We were going to talk to this farm’s caretaker in order to get information that would ostensibly help him in the long run, once it was incorporated into studies and papers, and those papers were shared at conferences, and those conferences were condensed for more general publications and the information within was applied to policy, or to the formation or further activities of an NGO, or something.  I wasn’t sure exactly how it worked. I was starting to wonder if it actually did. But I was sure I didn’t want to step on his cabbages.

            I was starting to wonder a lot of things, as we got closer to the husk-roofed house where our next interview subject was hopefully dry inside, hopefully not watching us and listening for the sound of cracking rain-swelled stalks and wincing.  Two semesters earlier, at my home institution, I’d done a big project on human-wildlife conflict for a conservation biology class.  It had involved entirely secondary research, which I’d clicked and paged together in safe, dry libraries free of predators, until I had enough for a long paper and an informative and interesting PowerPoint and a decent grade.  I thought I’d gotten and given a decent idea of things.  But now, as I pictured the rows of data that led me to my conclusions – clean data, penned into tables or stacked neatly into charts – I wondered how all those researchers had come up with those numbers, and what mud they’d had to rinse off. 

            We reached the house, checked our bootlaces for leaves, and began shouting hello.  Our man emerged rumpled; he’d been napping. We apologized for several minutes and then told him we’d come from far away, we were students, his input was valuable, since he was already up . . .

            The interview process was convoluted: I or a fellow student would ask a question, Moses would translate, the interviewee would answer, Moses would translate back, and we’d write it down.  That’s five steps, and an average of three tended to go wrong for every question.  We wanted to ask things that were not easily translatable – Swahili and English don’t necessarily contain all of the same ideas.  It took about fifty Swahili words to get across the concept of an endangered animal; people automatically assumed we were referring to animals that were dangerous to them.  I’m sure the same problem occurred on the way back, from Swahili to English, but I hit a blank when I tried to figure out what we must be missing or getting wrong when writing down their answers.  And even writing it down was problematic, today.  My waterproof notebook did not work as advertised, and all of our pens failed but one (Moses found it in his pocket.  It was Speedo brand, go figure).  Everything took longer because we had to shout over the rain, and felt like it took even longer due to the small waterfall colonizing the back of my neck.

            We took about forty minutes of the very patient, very sleepy man’s time, then moved on over the gentle and increasingly spongy hills.  Moses stopped us abruptly .  “Look!” he said.  We did. Farms spread out before us, red slugs of mud traveling between rows of plants, bringing water and nutrients, the land’s blood.  It really was beautiful.  I was about to say so when Moses pointed down at the closest farm. 

            “Our next stop.  That man grows pumpkins!  Elephants love pumpkins.  If you try to scare away an elephant, he will rather fight and die than leave if he is eating pumpkins.”  We were off again, sliding down the hill, leaving glossy streaks.  “That man will have much to tell us.”

            Elephants love pumpkins.  I’d had no idea; I’d learned something new.  That kept happening, as well, which was also problematic.  So many of hte most interesting (and, I was and am still convinced, useful) things people told us didn’t easily fold into the data.  How to reduce a story like the one we’d heard over and over that day – that of a man who, trying to protect his much-chewed-over cabbage patch from yet another herbivore, went after a marauding hippopotamus and got bitten nearly in half, and was waiting for death or a miracle in a Nairobi hospital while his surviving wife and daughters prayed over hospital bills they were too afraid to open– to one data point?  Fatalities caused by hippopotamus = +1?  To do that is to lose nearly everything that makes it worth it for this man’s neighbors to grit their teeth through yet another retelling.  Mistranslations or mishearings of questions can reveal more than the questions themselves; what does it say that the people we talk to are more immediately familiar with the idea of animals being dangerous to people than vice versa?  And circumstances surrounding an answer can qualify that answer, or render it irrelevant.  On a later transect walk, I very nearly escaped being attacked by dogs whose owner had trained them to be aggressive toward young white people after a volunteer group came through and “dug a hundred holes” around her farm.  Another time I talked to an old couple who playfully argued about the intrinsic benefit of wildlife, and it felt criminal to reduce all the things they’d said and the looks they’d given each other to two checks in different boxes on a data collection sheet.  One man who spoke very good English and gave suspiciously easy answers turned out to be a witch doctor.  He invited us back later to watch his wife predict the rains by throwing pebbles onto a design drawn on the ground.  How do you plot that on a graph?

            I came to Africa ostensibly to learn about science, and I did, I learned a lot.  But I also learned about its limitations, of the importance of what it doesn’t and can’t say.  I had to go back that night, and other nights, and condense what I’d learned into a set format, or plug it into a statistical analysis program.  So much was lost in the process of stripping it down to numbers or objective language, and I doubted it would magically return if what I’d written was ever brought back into the unabstracted, three-dimensional world in the form of a policy or an initiative.  When I look at data now, I try to see where it came from, and how much mud had to be scrubbed off before it could appear so stark and final.  Sticky red life-giving mud.

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