[IMCnetwork] Interpretive Quantification: HDR Conversation Over Coffee (Nov 5)

Wed Oct 13 01:29:07 AEDT 2021

Dear All,

For our upcoming Interpretivist HDR meeting on November 5, Matthew Robertson has kindly offered to facilitate a session on Interpretive Quantification.

I will send an email closer to the date to inform whether the meet up will be on zoom, in person or hybrid. Meanwhile, please see the email below and the readings he has recommended.

Thank you so much, Matthew!

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Interpretive quantification and the interpretation of quantification

I was very sorry to miss the last month's session on genealogy and am excited about this group.

Here are all the readings (and more): https://github.com/mpr1255/interpretive_quantification<https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fmpr1255%2Finterpretive_quantification&data=04%7C01%7Cheba.aladawy%40anu.edu.au%7C5f6e057fbf1f404e975e08d98d852338%7Ce37d725cab5c46249ae5f0533e486437%7C0%7C0%7C637696425230551339%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=7D3AgFTE6bl%2BaS8BGQg5OBJNNfrnmdNIy4udP3pq%2FQ8%3D&reserved=0>

The readings here look at closely related approaches to scholarship. I suspect the second half of the title -- the interpretation _of_ quantification -- may be more directly relevant to everyone, so I included more of that content. But I think Barkin and Sjoberg's pioneering work on interpretive quantification are well worth our time and energy too. Notes follow the readings. If you only have time for one, maybe skip straight to the Smith (2020) -- it's a case study, but is very theoretically rich may serve as a very useful model for other contexts.

Barkin, J. Samuel, and Laura Sjoberg. 2017. Interpretive Quantification: Methodological Explorations for Critical and Constructivist IR. Ann Arbor, MI: University of Michigan Press.

acknowledgements/introduction by authors: pp. xi-26,
then reflections by Patrick Jackson 227-242

-- This is the book that stakes out interpretive quantification as a mode of scholarship. It's full of case studies that put the method into action, but we can just read the introduction and Jackson's reflections at the end. The introduction explains how interpretive quantification fits in alongside the standard quant/qual positivist/interpretivist quadrant, filling a particular niche that is not often seen: the authors aim to "break down (false) assumptions that cause the tethering of methodology and method and to demonstrate the underutilized potential of quantitative, formal, and computational methods for constructivist and/or critical IR..." (p. 261; but this applies to all social science)

Protip: get a summary of the chapters in the book in the author's epilogue after Jackson's chapter.

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Porter, Theodore M. 1995. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton University Press.

pp. 33-45

-- In this chapter, Porter walks through some of the politics of quantification. Short and fun. I think one of the earliest books in this general area, followed just three years later by Desrosières's "Politics of Large Numbers", which shows that statistics as a discipline has always been central to state-building projects.

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Smith, Tobias. 2020. “Body Count Politics: Quantification, Secrecy, and Capital Punishment in China.” Law & Social Inquiry: Journal of the American Bar Foundation, May, 1–22.

-- One of the best case studies under the rubric of the interpretation of quantification. The paper discusses intense secrecy surrounding death penalty numbers in China, and explains how efforts to keep that secret -- i.e. prevent its accurate quantification -- lead to a range of distortive bureaucratic practices that cut against the state's other interests and reforms. It is also a joy to read so won't be a burden. If you only read one item, make it this.

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Bonus: a good attack on the 'idol' of a universal method of scientific inference (stats): Gigerenzer, Gerd, and Julian N. Marewski. 2015. “Surrogate Science: The Idol of a Universal Method for Scientific Inference.” Journal of Management 41 (2): 421–40.

-- not on the reading list but just worth highlighting. This is a fantastic explanation of why science does not have a theory of confirmation, why statistical inference is not the panacea, and why it won't/shouldn't be replaced by Bayesianism, which also attempts to precisely quantify uncertainty. Just shows that nothing will escape philosophy -- and it's always most fun when it's the statisticians saying that.

The readings here look at closely related approaches to scholarship. I suspect the second half of the title -- the interpretation _of_ quantification -- may be more directly relevant to everyone, so I included more of that content. But I think Barkin and Sjoberg's pioneering work on interpretive quantification are well worth our time and energy too. Notes follow the readings. If you only have time for one, maybe skip straight to the Smith (2020) -- it's a case study, but is very theoretically rich may serve as a very useful model for other contexts.