Another common theme frequently explored in the science communication literature is one of risk. Beck (1992) coined the notion of the risk society as a postmodern (or second-modern) theoretical move – a move that theorized and emphasized (1) a reflexive modernity that questions the role of cultural values in the presentation and proposed implications of scientific communication of risk and (2) the unequal distribution of risk as a more powerful explanatory variable in the structuration of society compared to traditional Marxist readings of wealth as such a variable. Put differently, “even in their highly mathematical or technical garb, [science communicators’] statements on risk contain statements of the type that is how we want to live” (Miller, 2008, p. ) Through this frame of risk, Beck argues that the values latent within risk statements casts science not as a superior discourse of objective insight, but as one of many discourses within a society of plural discourses. Such statements of risk thereby impinge on the ethical – a rhetorical shift away from scientists like Katz or Sheckman (see Section 2.2.1) who would likely advocate for the uninterested sharing of knowledge over advocating for a particular course of action.
For example, suppose a science communicator advocates for the use of vaccines by countering the perceived risks of a vaccine-hesitant audience through a series of statistics. For instance, vaccines only cause [insert medical complication de jour] at a rate of one per million doses. From a deficit perspective, one would view this messaging as effective—i.e., exchanging correct scientific information to overwrite what might otherwise be an over-salient risk among vaccine-hesitant audiences. However, from a risk society point of view, such a presentation does not account for the values or the scientific skepticism held by such audiences. In other words, such statements obscure or hide embedded cultural values such as (1) the primacy of statistical information as convincing evidence; (2) the unqualified positive affirmations of vaccines as a tool to promote public health. This approach to communication, moreover, does not account for dormant, unexpressed skepticism by audiences —such as skepticism towards the U.S. healthcare system or the role of competing values-based voices. For example, anti-vaccine influencers often stress the sanctity of parenthood and promote the discourse that no risk is too small when it comes to the safety of one’s children. Put differently, science skepticism is not limited to outspoken political speech, but also quietly situates itself within shared community assumptions and values.
Wynne (1996) presents one of the seminal ethnographic studies at the intersection of science communication and risk society. In a study conducted among sheep farmers in Cumbria in the northwest of English, Wynne investigated trajectories of communication between scientists and local sheep farmers about the potential risks of radioactive cesium – cesium hypothesized to be fallout from the Chernobyl disaster. Contrary to an embrace of uncertainty articulated by Katz, Wynne found that scientists’ public communications of safety recommendations papered over underlying uncertainty with performed surety. For example, the scientists confidently and publicly reported the results of soil samples while ignoring internal disagreement about the validity of their soil sampling methods or other methodological procedures – procedures that farmers themselves contributed to as the following quote demonstrates:
The scientific knowledge constructed out of field observations began life as highly uncertain and uneven – the farmers watched scientists decide in apparently arbitrary ways where to sample mountainsides or fields with huge variations of readings, and they helped scientists as they changed their recorded monitoring readings of sheep contamination by changing the background reading, or the way the monitor was held to the sheep. Yet these kinds of uncertainty and openendedness were obliterated by the time the knowledge returned to that same public as formal scientific knowledge in official statements (Wynne, 1996, p. 66, emphasis added).
This adds a layer of pragmatism to Bruner’s paradigmatic-narrative divide. While many scientists express skepticism toward storytelling due to the tendency of storytellers to condense, simplify, and embellish information based on the imagined perspective of a reader, many scientists participate in this practice when it comes to communicating safety information to the public. In other words, how can I, as a science communicator, share insights that are clear and actionable without burying my audience in nuance and minutiae? Or, as Beck might ask, what political, ethical, and onto-epistemological commitments underline my message? And how might my audience approach my messaging from a different perspective?
Wynne, along this line of inquiry, further argues that sheep farmers’ mistrust of scientists’ data and recommendations came not from a lack of information, but rather from a mismatch of knowledges. Sheep farmers’ knowledge of their land was more local, intuitive and relational compared to scientists’ more generalized and abstracted knowledge. The scientists therefore often “overlooked the essentially localized nature of [shepherds’] knowledge, because clay soil was not a universal condition, and in other soils such as those in the hill areas, very different behavior” (Wynne, 1996, p. 62). This tension extends beyond the natural world to also produce conflicting models of the human – a relational, specific, intuitive human and an isolated, generalized, evidence-based human. This conflict, Wynne argues, contributes to scientific skepticism more than just the truth or falsehood of informational propositions. Read against Beck’s arguments of reflexive modernity, we see commitments to paradigmatic, scientific, or narrative modes also constitute the character and values of a discourse. There is no neutral way to communicate knowledge. Or as Wynne (1996, p. 55) argues, the most fundamental dimension of risks – a dimension deficit approaches ignore – is the “risk to social identity which is felt to be involved” in one’s dependency on science and science’s underdeveloped “models of the human and the social” when compared to more embodied and intuitive ways of living.
Ontogenetic Log
Listening back to this discussion of the risk society takes me to AI. My LinkedIn feed is absolutely swamped with alarm bells ringing about AI — not only the ways that it destroys thinking and perverts discourse and otherwise makes researchers worse, but also that it has disastrous effects for the environment in which we live. I think that Beck’s book almost perfectly maps onto the current milieu when it comes to AI and the discussion of its risks.
In my ESL class, one of my students in a metaphor task described AI as an enabler. I thought this was beautiful. It draws out two definitions of the word “enable” — one quite positive, one exceedingly negative. In the first sense, AI can enable us to do tasks that we may not have been able to do on our own or without assistance. My use of AI has transformed my ability, for example, to study Chinese characters and to make sense of the philosophy of Zhuangzi. Is it 100% accurate? I’m not sure. Does it make sense to many of the Chinese colleagues to whom I explain my emerging ideas? Yes, it does. In this case, AI does raise the floor of our ability to learn new knowledge — if we can engage with it intentionally, with a process-oriented goal of learning as opposed to a product-oriented goal of completing something.
This unfolding process of generating knowledge, and the closure of product, actually maps quite well onto the Chinese characters of 生 (shēng) and 成 (chéng) — but I’ll talk more about that at a much later date.
Now, in terms of environmental degradation, I don’t have much to say except that I think it’s bad. And honestly, I do think that there is a global economic collapse that will probably precede the maximization of the environmental degradation that AI will wreak. In other words, AI is going to destroy humanity economically before it does so environmentally.
But one thing I really want to speak to in our field is the ways that so many researchers wring their hands over what AI means for researchers and their writing. When you look at the explosion of submissions received by top journals — we’re talking about 300% to 400% increases — we can clearly see that AI is having an impact on how many manuscripts scholars can submit, and consequently how much more slowly editors need to process this overwhelming deluge. I don’t have a smoking gun to say this is definitively because of AI, but if you check the smoke, you know.
I again think that the way out of this problem requires storytelling. I’ve been playing with a Chinese compound: 生文 (shēngwén). 生, as a character, carries indexicalities of life, but also of imminence, of unfolding production of nature, and of unlimited potentialities being realized in the present moment. 文, on the other hand, represents the scholarly institution, academic knowledge, books — book learning, the educated. It is a concept often associated with scholarly pursuits, and ultimately with words for culture, writing, or text. I’ll explain more in depth in a later post, but ultimately I see 生文 as the tension that academics need to negotiate between writing out the vitality of their research while also needing to deal with the constraints and closures — 成 — enforced by institutional demands, policies, and agreed-upon writing conventions.
The biggest irony about 生文 is that when you search it on Chinese search engines, you often get links to AI-generated text, because 生文 is often used these days as an abbreviation for 生成文本 (shēngchéng wénběn) — literally, “text generation.” Yet I would argue that the data from which AI writes is always 成. Yes, it might be an open system that is constantly trying to hoover up more data, but it can never be an open system of creativity in the ways that a human can be — at least not as AI is theorized now. AI can only operate by the conventions of those who wrote before it.
And this is where storytelling becomes crucial for us as researchers: to step outside of the binary, back-propagating neural network, and to write from a place of imminence, of vitality, and therefore against linearity. This is my call for researchers and writers. Don’t abandon convention — but don’t take it so seriously, either. This is what Zhuangzi might tell us. We must be legible, but not too legible. We must, in other words, not take the conventions so seriously that enable us to choke ourselves into a sarcophagus of our own making.
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