Mapping Metaphor across Big Data, Biotechnology, and Genome Sequencing

Everyday metaphors

Before I was geeky about science I was geeky about words. For my 16th birthday, my best friend gave me the “Encyclopedia of Etymology” — a giant tome about the origins of words (not bugs, people! That’s entomology). So of course I get excited when science and language interact, which happens a lot with metaphor. I even did my Master’s research thesis about metaphor (more on that later). One of the most surprising things I learned early on in that project was that most metaphor is actually lurking beneath the surface of how we talk and think on a daily basis, rather than being mostly confined to speeches and fancy poems (e.g., “Shall I compare thee to a summer’s day?”).

An example of a quite basic metaphor is that up is good and down is bad. Would you rather have things “looking up” or to be “feeling down”? Granted this metaphor may not hold across cultures, but in Western societies it is so ingrained as to almost be invisible. Note I did not discover all of this, but rather was introduced to these ideas in Lakoff and Johnson’s seminal 1980 book “Metaphors We Live By”. Think of Lakoff and Johnson like the Watson and Crick of modern metaphor studies. (If there is a Rosalind Franklin out there in this analogy, then my apologies in advanced for the omission!)

Metaphors for “big data” – h/t to Sara Watson

Metaphor is subtly sprinkled throughout our daily speech, and it can have powerful effects on how we think and act. Which is why it’s so important to identify metaphor and understand its sway on us. So I was pleased to recently come across self-proclaimed “technology critic” Sara Watson’s article on dominant metaphors for big data. She does a lovely job of breaking down dominant industrial metaphors for big data and suggests that replacing them with embodied metaphors, those more tied to our lived experience — our physical bodies — might help people exert more control over data and its downstream uses.  Otherwise big data becomes this inevitable industrial, machine complex bearing down on us, so better hop on board or get out of the way.

Today’s society has a borderline morbid fascination with big data, which I’ve also written about previously in “Big Data, Big Deal?”, and you can see how the dominant metaphors perpetuate this fascination.  A particularly problematic metaphor in my mind is that of data as a natural resource that should be mined, extracted, and purified. In this construct, data are commodified and spatialized. Just think of all the untapped reserves of “raw” data waiting for the boldest and most pioneering person to tap into: data logged daily by our smartphones, our Facebook profiles, and even our very bodies. In this metaphor, data become pre-factual and given, rather than contextual and imagined (whereas in actuality you have to conceive of something as a data point before you collect it — aha, even there,  I did it: “collect data” as if I was picking wild huckleberries on a mountainside…which I recently did, incidentally). But full circle back to etymology: the very word “data” is from the Latin verb for “to give”….so it’s not totally our fault that it’s easy to take data as “a given.” (More on other cool things you can learn about the word “data” in my earlier post.)

The need to tease out metaphorical concepts

Sara Watson’s article articulates metaphors as “metaphorical concepts”, or “X is Y”: e.g., “Data is a natural resource.” Formulating metaphor this way is helpful in understanding the consequences or “entailments” of the metaphor and to raise further questions. If data is a natural resource, is it a renewable one or something finite (e.g., fossil fuel) that we may run out of? If data is a natural resource, who is “mining” it and who is using or buying it?

Metaphorical concepts are rarely stated outright, but identified through analyzing different expressions of the metaphor. You can see these expressions listed under the heading of the metaphorical concepts in Watson’s article: words like “raw,” or “trove”. Analysis of metaphor involves picking out those instances and then drawing out the underlying metaphorical concept.

Critique of a CRISPR metaphor analysis

Metaphor analysis that stops short of articulating metaphorical concepts is less useful. Last fall I wrote a piece along with two of my thesis committee members critiquing a metaphor analysis of the gene-editing system CRISPR that had this very problem. We argued that failing to articulate underlying metaphorical concepts resulted in a missed opportunity to understand who uses CRISPR to do what? Is CRISPR, as a technology, the subject of the metaphor or is the scientist using CRISPR the subject? It’s an important question of who or what has the agency to act and make decisions about gene editing.

Also, because the authors didn’t identify metaphorical concepts, most of the metaphors they report were about the genome itself rather than about CRISPR. It would have been easier for them to draw robust conclusions about CRISPR metaphors if they’d been able to separate out genome metaphors (to separate the “text” from its “editor,” as we allude to in the title of our critique).

Metaphors about genome sequencing: my MPH thesis

Oh – and did I hear someone ask about my Master’s thesis? I’m going to assume that’s a “yes.” For my Master’s in Public Health degree in Public Health Genetics, which I completed Spring 2014, my thesis project was a metaphor analysis of research participants discussing whole genome sequencing. I was fortunate enough to have access to several transcripts from previously conducted interviews and focus groups where people were asked to discuss genome sequencing in the context of research and medicine. No one was asked about metaphors specifically, but because of the frequency of underlying metaphors in spontaneous speech, instances of them popped up often in the participants’ discussions.

One of the most common metaphorical concepts I identified was “Genetic information is a weapon.” In some cases, getting personal genetic information was seen as a weapon in the hands of the individual, something empowering them to act, to defend themselves against disease or other potentially negative experiences. For other people, the weapon metaphor was one where genetic information was used as a weapon against them, to knock them over or leave them “shell shocked.” So even the same metaphorical concept can have different  consequences, here depending on what  or who is in control of the information.

Full disclosure was that initially I wasn’t forming my results as metaphorical concepts (“X is Y”) but more like keywords or domains (as we later critiqued in the CRISPR metaphor analysis). My committee member and resident metaphor expert, Leah Ceccarelli, strongly encouraged me to find the metaphorical concepts. My only real objection was “that sounds hard” (remember I’d never done formal metaphor analysis before), so once I realized that was lame I made myself do it – and ended up with a much stronger project for it.

You can read my whole thesis on ProQuest: search for title “Mapping Metaphor: A qualitative analysis of metaphorical language in discussions of receiving exome and whole genome sequencing results” (or, if you don’t have access to ProQuest, I’m happy to email it!). I also had peer-reviewed journal article published here. (Yes, it took an extra ~18 months to have that paper see the light of day – see my earlier discussion of the iterative and often trying nature of scientific publication here.) Meanwhile, here’s a table summarizing the main metaphorical concepts I identified.

[table id=1 /]

Other recommended reading:

Ceccarelli, L. (2013). On the Frontier of Science: An American Rhetoric of Exploration and Exploitation. Michigan State University Press.

Condit, C. M. (1999). The meanings of the gene: public debates about human heredity. Madison: University of Wisconsin Press.

Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.

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