Writing Assignment-Week 2(1/23)
Ethnography for a data-saturated world. Manchester University Press. 2018. Introduction: Ethnography for a data-saturated world.
[Key arguments/themes/methods/terms]
Quantitative(numbers) analysis vs Qualitative(narrative) analysis
“Numbers and qualities are not inherently opposite ways of seeing the world”
→Analyze the issue from two types of perspective.
→How these two disciplines have incorporated in the real-world setting. Any example?
Digital Anthropology
"Study the significance of digital phenomena, which serve both as object of ethnographic enquiry - what happens in online communities, or in data - mediated interactions- and as a methodological puzzle about how come to understand those social worlds."
→How people behave differently in digital culture.
→Does the more use of the Internet only accelerate the echo-chamber?
[Two key questions]
1. What makes the bunch of unorganized data into insight and value?
2. We are exposed to a huge amount of information every day.
Who stewards and take initiative to make the public data for good use?
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2.Francisca Grommé, Evelyn Ruppert and Baki Cakici.
Data scientists: a new faction of the transnational field of statistics.”
[Key arguments/themes/methods/terms]
What makes data scientists apart from conventional statisticians?
How they (statistics/computer science) are different, or a matter of job title?
If they have some elements in common, how those elements intertwined with each other?
Dynamic shift in data resources, from survey/census to 3Vs(variety,velocity,volume)
-Analysis by Walter Radermacher-
| Day | Era | Feature |
| 19c | Statistic 1.0 | Birth of statistic |
| 20c 1st half | Statistic 2.0 | Move to mathematics, inference, surveys and sampling |
| 20c 2ndhalf | Statistic 3.0 | Introduction to new technologies and IT infrastructures |
| 21c | Statistic 4.0 | ‘Data revolution’: big data, machine-to-machine communication and modeling. |
1. How computer scientists work differently, when they working with official data.
2. Not everybody can become data scientist, but data savvy researcher/business person is expected in more and more field. What is the required/preferred skill sets that person without the background expected to have to make use of data for their own expertise?
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3.Michel Foucault, “Nietzsche, Genealogy, History” (1977)
[Key arguments/themes/methods/terms]
Nietzsche’s challenge to the pursuit of origin
“What is found at the historical beginning of thing is not the inviolable identity of their origin; it is the dissension of other things. It is disparity”
Possession of history
“Rules are empty in themselves, violent and unfinalized; they are impersonal and can be bent to any purposes. The successes of history belong to those who are capable of seizing these rules, to replace those who had used them, to disguise themselves so as to pervert them, invert their meaning, and redirect them against those who had initially imposed them”
→Who benefits from history?
“Effective” history
“ ‘Effective’ history deprives the self of the reassuring stability of life and nature, and it will not permit itself to be transported by a voiceless obstinacy toward a millennial ending. It will uproot its traditional foundations and relentlessly disrupt its pretended continuity. This is because knowledge is not made for understanding, it is made for cutting.”
→Reframe history as a seamless continuity from the past
[Two key questions]
1. How marginalized people who have no power over seizing rules can benefit from history?
2. Can we improve the way we learn history in the school setting and incorporate the essence of effective history?