November 7th, 2011 at 5:00 pm edit
This weeks reading reinforced in me a feeling that certain research methods are more narrow in their approach, and this characteristic can be both limiting and powerful. I come to this class as an applied physicist. My personal motivations are less focused on unlocking the secrets of the natural world and more focused on using physical models to produce objects that provoke societal change. I agree with concepts along the lines of “inertia of belief,” but I also sympathize with Pickering’s point of view. Every bit of understanding that we have is based on some model that may or may not be grounded in some experiment or academic discipline. As long as these models help us cope with the world, make solar cells, or whatever, it may not matter if they were produced by insular, self-perpetuating groups and ideas. The simple concept of reproducibility is often enough to evoke meaningful change. Sometimes it is not, and that’s why I am also pursuing other research approaches. Everything is an approximation, and the value of knowledge depends on our particular motivations and interests. I think many of problems arise when certain forms of knowledge are vaulted and others are ignored. One of my favorite aspects of Science and Justice is that it is a group of people who are interested in developing and acknowledging a diversity of knowledge sets and research approaches.
Kathleen Uzilov Says:
November 7th, 2011 at 7:31 pm edit
I think what makes the Millikan story so interesting, is that of the two competing rivals, the one whose work was accepted and is taught to millions of students today is actually the one whose work seemed more biased and less objective, perhaps even less ‘scientific’. If it had gone the other way, and Millikan’s ideas had been dismissed, I suspect it would have been perceived as almost expected. That’s why it is such an illustrative case, because it shows the usefulness of presuppositions, despite the fact that presuppositions in science can feel a bit uncomfortable.
I really enjoyed the way all of these articles built on one another. I think, in the end, for me it came down to the idea that “Without presuppositions, experiments can neither start nor finish” (Galison, 13). It might not seem ideal, and it perhaps doesn’t adhere to the scientific method as cleanly as some (inside and outside of scientific academia) may assume science does. But the reality is that science is continually growing in a certain direction in large part based on factors outside of pure facts, including which ideas are popular at the time and what has already happened. The history of scientific progress is somewhat path-dependent, and could have potentially gone other ways. ‘What science says the world is like at a certain time is affected by the human ideas, choices, expectations, prejudices, beliefs, and assumtions holding at the time’ (Machamer quoted in Niaz, 700). Does that mean it cannot still be useful, or that all science is merely relative? I don’t personally think so.
November 7th, 2011 at 11:22 pm edit
Reading about the Millikan oil drop experiment was very interesting to me because in biology and specially ecology, the goal is rarely to determine something so exact and universal as the charge of an electron. Ecological phenomena tend to be more variable, messy, and context-dependent than what seem to me the less changeable and more specific phenomena investigated by physicists. In addition, many investigations are, by necessity, observational rather than experimental. Still, it is easy to see how human presuppositions have come into the kind of work that has been done in the field. For example, up until the 1970s, ecologists often viewed humans as outside actors whose influence could pollute the study of “real” nature, and focused their studies on “untouched” systems. However, in the decades since, ecologists have come to see humans as important parts of ecosystems that must be studied and accounted for, and have further realized that there is no such thing as a pristine system unaffected by anthropogenic factors. Presuppositions also come in when trying to find patterns in (almost inevitably) messy ecological data. Though it is often acknowledged that variation is the core of biology, it can be difficult to parse out what variation is meaningful to the question being investigated, and how. Presuppositions about the importance and meaning behind variation often seem to inform whether ecologists focus on the greater trend of the variation in their work.
It’s interesting to compare this week’s readings to Richard Feynmen’s (1974) take on the Millikan experiments. He said
“We have learned a lot from experience about how to handle some of the ways we fool ourselves. One example: Millikan measured the charge on an electron by an experiment with falling oil drops, and got an answer which we now know not to be quite right. It’s a little bit off because he had the incorrect value for the viscosity of air. It’s interesting to look at the history of measurements of the charge of an electron, after Millikan. If you plot them as a function of time, you find that one is a little bit bigger than Millikan’s, and the next one’s a little bit bigger than that, and the next one’s a little bit bigger than that, until finally they settle down to a number which is higher.
Why didn’t they discover the new number was higher right away? It’s a thing that scientists are ashamed of–this history–because it’s apparent that people did things like this: When they got a number that was too high above Millikan’s, they thought something must be wrong–and they would look for and find a reason why something might be wrong. When they got a number close to Millikan’s value they didn’t look so hard. And so they eliminated the numbers that were too far off, and did other things like that. We’ve learned those tricks nowadays, and now we don’t have that kind of a disease.”
It seems like, contrary to Feynman, we will always have “that kind of disease,” but acknowledging and examining it can lead to insights about the assumptions, biases, prejudices and other human elements that are part of science, and thus can give us a fuller understanding of scientific results.
November 8th, 2011 at 8:22 am edit
I found these readings really interesting, particularly Gallison’s work on how experiments end and the image and logic traditions. Interestingly, both points speak to the work of anthropologists, who often have a strange relation to fieldwork closure and the question of the image-logic dyad.
Anthropologists, like all putative scientists, are forced by funding, professional demands, and any number of other factors to stop researching at some point and begin writing up. Because our work is almost never experimental, and because we often maintain communication with our ethnographic subjects, this is no clear transition. The lives of our subjects is the focus of our inquiry, and these keep on going after we leave; there is always more important data, there are always more questions that could be asked, and there are always gaps that could be filled in our fieldnotes.
Anthropologists also have taken varied perspectives on the question of image and logic. There have been entire books (ethnographic monographs) devoted to a single ethnographic subject, while other authors write in an ambiguous third person about an amorphous “group” of people. In the case of the former, the single subject is considered to be representative of many, typically by virtue of being an interesting and complex figure whose personality or biography captures something significant about a larger group. In the case of the latter, we expect that the researcher has spoken with a large number of people and spent a sufficient amount of time with the “group” about which s/he speaks, and that s/he makes responsible caveats. Neither of these are, of course, perfect, particularly if you’re in the business of making Borgesian maps, which I believe we all are at some level.
I would be curious to hear how other disciplines have approached these two questions (ending and image-logic)?
November 8th, 2011 at 9:53 am edit
This week’s readings conjured in my mind groupings and lineages and webs of Educated. White/Western. Men. A particular class of scientists — a peer network with apparatuses of peerhood — that believed or had been trained to believe (perhaps continue to be trained to believe) in the ability to structure, restructure, frame, and reproduce a particular kind of World. How might we consider worlds — small “w”, plural — otherwise? And a question Jenny asked a couple weeks ago is still on my mind: what is the project? For Millikan and Ehrenhaft? Niaz, Holton, Franklin, Barnes, Goodstein? Gallison? How does our experiment, our attempt to historicize knowledge-making practices, end? What does feminist science studies enable, trouble?
I appreciate very much Gallison’s “Image and Logic”. On frontispiece: “…Who is the ‘experimenter’ whose activities we have been discussing?… The experimenter, then, is not one person, but a composite…. He is a social phenomenon, varied in form and impossible to define precisely.” Gallison’s description of “two competing traditions of instrument making” is particularly generative for me: image or mimetic representation of a golden event; and logic or statistical calculation of “the billionfold background”. He uses these to describe scientific practices, but I’m interested in how this distinction translates to current projects of data visualization. Gallison’s book was published in 1997, and I wonder whether this distinction might still hold today, with increases in computing power that enable synthesis of this “billionfold background” into a singular image (number and graphical patterns being types of image). Importantly, this brings up Barad’s question: what kinds of agential cuts do these different apparatuses enable? And which cuts matter, for whom?
(apologies this is quite late… internet issues, couldnt upload…)
November 8th, 2011 at 10:03 am edit
Reading again, perhaps worth pointing this out from Gallison (Image and Logic, p. 30): “At the level of material culture, there is a large-scale structure to the history of experimentation, the long-term history of image and logic.” The question of scale returns! This relies on a distinction between small and large, singular and multiple multiples, currency and history. Barad’s “phenomena” troubles this radically. Would love to discuss if possible.
November 8th, 2011 at 1:18 pm edit
The Milikan and Ehrenhaft Dispute brings up several issues that the laboratory research delivers. In my experience these issues seem to mask themselves as moments of panic. When one encounters anomaly in our dataset, a scientist may reason that human error occurred in the performance of data production. Human error seems to be a way for scientists to explain away and disappear evidence for that could actually elucidate new explanations for the data phenomena.
One can never do something the same thing every time, we do our best, but if in the process of creating data, one is aware of such mistakes, we, at least I do, make note of the mistake, carry the process through, measure it. If I were to get to the analysis, I would note how such data collected from that instance differs from others. I would throw that data out of my set, but also document that I had done it, and the reasoning behind ignoring. I’ve come across such moments that resulted in panic. A group of us, undergraduate researchers, forgot to label a specimen of tissue placed on a slide to be stained with pigments, we tried to track the origin of the slide, thankfully one of us had the integrity to propose we make note of it in our notebooks, continue working with the slide, for curiosity’s sake, and stop looking for the slide’s origin. We agree and then documented that we discussed such matter. I like that about lab notebooks. I currently do not have a lab notebook, I am not in the lab often enough to take on a notebook, I just collect specimen and others collect measured data. I like the notebook, its a document of history. We often forget what happened in the process of getting information (this is also the case for interviews) and then once it comes to writing or analysis, we forget what were supposed to remember.
The lab notebook is also a protective document and can save us from being the scapegoat. Perhaps someone did not train us properly, if we document our daily tasks, as we did in my first lab, one can potentially find the source of miscommunication that we may designate as anomalies in data collection.