UChicago-Computational-Content-Analysis / Readings-Responses-2023

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3. Discovering Higher-Level Patterns - [E2] 2. Nelson, Laura K. 2021. #43

Open JunsolKim opened 2 years ago

JunsolKim commented 2 years ago

Post questions here for this week's exemplary readings: 2. Nelson, Laura K. 2021. “Cycles of Conflict, a Century of Continuity: The Impact of Persistent Place-Based Political Logics on Social Movement Strategy.” American Journal of Sociology 127 (1): 1-59. (But you must also skim the framework she draws from her analysis in Nelson, Laura K. 2017. “Computational Grounded Theory: A Methodological Framework.” Sociological Methods & Research DOI: 10.1177/0049124117729703: 1-40.)

sizhenf commented 2 years ago

This is a very interesting paper! There author uses text analysis methods in arguing that some core differences in the feminist movement in New York and Chicago can be explained by the differences in the culture and politics in these two cities. While I'm very fascinated with the argument, I'm also interested in if there's any alternative explanation that can account for the differences. In particular, papers that talks about the "place effect" of certain phenomenons sometimes also talks about the "individual effect" - the innate factors of the subject that can contribute to the differences. In this case, I'm curious about what the individual effect is how it contribute to the differences in the evolution of the movements

Emily-fyeh commented 2 years ago

Finally officially reading this piece since the last time reading about it in the fundamental materials. I think the work is very intriguing, for it clarifies the historical context of the "place" of the feminist organizations in Chicago and New York. I think the proportion and role of STM are reasonable, as it can weigh each topic from the corpus, which makes it a preferable choice to LDA. (Also, the final remarks on future research directions are also promising.)

facundosuenzo commented 2 years ago

I found both papers (Nelson, 2017 & 2021) exciting and insightful. In her 2017's computational grounded theory, she argues that it "is more valid than traditional grounded theory, which asks the reader to simply trust the representativeness of particular examples or quotes." Then, in her empirical research of 2021, she writes, "I found that the specific issues addressed and the framing of ideas—the content of their claims—did indeed change between the two waves of mobilization, as described by historians" Thus, my question is: is that one approach, let say computational, is "more valid" than the other if she in some way confirms previous historical research? What seems undoubted is that get a "paradigm shift," we probably will need both grounded theory computational theory (and abductive logic). Second, I didn't find compelling the alternative explanations she presents in the discussion to address WHY these two places differ in terms of their institutional configurations. Some of these possible alternatives should have been incorporated into the models or explored more deeply in terms of the history (but this is just criticizing from outside of the text).

ttsujikawa commented 2 years ago

The reading is pretty interesting. She attempts to analyze the feminist movement with two distinct axes of place and wave. Though her finding is highly contributive and exciting, I am not fully convinced how these similarities/differences among different cities/waves have been constructed. I think that the core of this work could be answering that question and diving deeply into her datasets possibly answers the question.

sudhamshow commented 2 years ago

Several interesting findings from the paper. The author emphasises the existence of social structures in social movements and its rooting in long standing social changes. I was wondering if the same strategy of policy-oriented community organising is still prevalent in Chicago. Don't these underlying values erode over time based on the necessities of the generation?

The author also alludes to the importance of 'expert knowledge' in finding interesting patterns and how one cannot learn theory merely based on data.