phnk + soc:quantification   181

Juking the Stats? Authoritarian Information Problems in China | British Journal of Political Science | Cambridge Core
"Economic statistics inform citizens of general conditions, while central leaders use them to evaluate local officials. Are economic data systematically manipulated? After establishing discrepancies in economic data series cross-nationally, this article examines Chinese sub-national growth data. It leverages variation in the likelihood of manipulation over two dimensions, arguing that politically sensitive data are more likely to be manipulated at politically sensitive times. Gross domestic product (GDP) releases generate headlines, while highly correlated electricity production and consumption data are relatively unnoticed. In Chinese provinces, the difference between GDP and electricity growth increases in years with leadership turnover, which is consistent with juking the stats for political reasons. The analysis points to the political role of information and the limits of non-electoral accountability mechanisms in authoritarian regimes."
world:china  soc:quantification  via:henryfarrell 
18 days ago by phnk
The Desperate Quest for Genomic Compression Algorithms - IEEE Spectrum
"This vision will require an almost unimaginable amount of data to be stored and analyzed. Typically, a DNA sequencing machine that’s processing the entire genome of a human will generate tens to hundreds of gigabytes of data. When stored, the cumulative data of millions of genomes will occupy dozens of exabytes."

Well discussed and explained at
biology  data:management  soc:quantification 
august 2018 by phnk
The Great Regression. Machine Learning, Econometrics, and the Future of Quantitative Social Sciences
"What can machine learning do for (social) scientific analysis, and what can it do to it? A contribution to the emerging debate on the role of machine learning for the social sciences, this article offers an introduction to this class of statistical techniques. It details its premises, logic, and the challenges it faces. This is done by comparing machine learning to more classical approaches to quantification – most notably parametric regression– both at a general level and in practice. The article is thus an intervention in the contentious debates about the role and possible consequences of adopting statistical learning in science. We claim that the revolution announced by many and feared by others will not happen any time soon, at least not in the terms that both proponents and critics of the technique have spelled out. The growing use of machine learning is not so much ushering in a radically new quantitative era as it is fostering an increased competition between the newly termed classic method and the learning approach. This, in turn, results in more uncertainty with respect to quantified results. Surprisingly enough, this may be good news for knowledge overall."
stats:machine-learning  soc:quantification 
august 2018 by phnk
[1706.01099] Latent Estimation of GDP, GDP per capita, and Population from Historic and Contemporary Sources
"The concepts of Gross Domestic Product (GDP), GDP per capita, and population are central to the study of political science and economics. However, a growing literature suggests that existing measures of these concepts contain considerable error or are based on overly simplistic modeling choices. We address these problems by creating a dynamic, three-dimensional latent trait model, which uses observed information about GDP, GDP per capita, and population to estimate posterior prediction intervals for each of these important concepts. By combining historical and contemporary sources of information, we are able to extend the temporal and spatial coverage of existing datasets for country-year units back to 1500 A.D through 2015 A.D. and, because the model makes use of multiple indicators of the underlying concepts, we are able to estimate the relative precision of the different country-year estimates. Overall, our latent variable model offers a principled method for incorporating information from different historic and contemporary data sources. It can be expanded or refined as researchers discover new or alternative sources of information about these concepts."
soc:quantification  econ:macro 
june 2018 by phnk
Banlieues : deux députés relancent le débat sur les statistiques ethniques
"Chargés d’interroger l’action de l’Etat dans l’exercice de ses missions régaliennes dans le « 9-3 », les auteurs confient avoir été confrontés, « dès les premières auditions », à « une méconnaissance de la population », à commencer par le nombre réel d’habitants du département francilien."
fr:ethnic-politics  soc:quantification  fr:immigration 
may 2018 by phnk
Why America Fails at Gathering Hate Crime Statistics — ProPublica
"According to federal records, the Huntsville Police Department has never reported a hate crime."
soc:quantification  violence:crime  violence:racism  violence:police 
december 2017 by phnk
The Media Has A Probability Problem | FiveThirtyEight
"The media’s demand for certainty — and its lack of statistical rigor — is a bad match for our complex world."
stats:probability  journalism  soc:quantification 
november 2017 by phnk
research – DATACTIVE
"Data activism: The politics of big data according to civil society is a research project based at the Department of Media Studies of the University of Amsterdam. It is funded by a Starting Grant of the European Research Council (StG-2014_639379 DATACTIVE), with Stefania Milan as Principal Investigator."
soc:quantification  data:availability  at_a_loss_for_tags 
november 2017 by phnk
When Correlation Is Not Causation, But Something Much More Screwy - The Atlantic
"Your typical social science graduate curriculum talks a lot about getting standard error right but on a day to day basis most of our work goes into getting the data into the proper form and this is also where most problems come from." -- Amen.

Contains a moderate amount of Judea Pearl.
stats:measurement  soc:quantification  stats:correlation  epistemology:causality 
september 2017 by phnk
Engineering the public: Big data, surveillance and computational politics | Tufekci | First Monday
"Digital technologies have given rise to a new combination of big data and computational practices which allow for massive, latent data collection and sophisticated computational modeling, increasing the capacity of those with resources and access to use these tools to carry out highly effective, opaque and unaccountable campaigns of persuasion and social engineering in political, civic and commercial spheres. I examine six intertwined dynamics that pertain to the rise of computational politics: the rise of big data, the shift away from demographics to individualized targeting, the opacity and power of computational modeling, the use of persuasive behavioral science, digital media enabling dynamic real-time experimentation, and the growth of new power brokers who own the data or social media environments. I then examine the consequences of these new mechanisms on the public sphere and political campaigns."
soc:quantification  via:henryfarrell 
september 2017 by phnk
Steve Ballmer believes that facts about government spending can anchor public debate. Here’s how. - The Washington Post
"If a business is going to do things sensibly, it has to reconcile its data. The SEC [Securities and Exchange Commission] requires that businesses reconcile their data. Auditors insist that businesses reconcile their data. Our government should reconcile its data."
soc:quantification  data:availability  stats:measurement 
august 2017 by phnk
Steve Ballmer has a big idea: to be a partisan for the facts - The Washington Post
"We’re not in the forecast business. Lots of people can make different forecasts based on the history and the policy. The CBO [Congressional Budget Office] does it — they’ve been a respected source for a number of years. Early in my retirement, I visited the CBO and sat down with Doug Elmendorf and his staff and was impressed by them and what they do. Obviously, the OMB [the president’s Office of Management and Budget] has staff to do these things, and there are various others such as the JCT [Joint Committee on Taxation]. But they’re forecasters. Forecasters have an easy time disagreeing with each other. When it comes to the past, the biggest issue is reconciling the data that exists, making it more timely, and getting into a position where we’re not revising it continuously. There is a significant amount of data that changes after the fact — which is published and then changed. We change it on our site — that probably disturbs many people and makes them worry about the integrity of the data that is continually being revised. Some of these things are done with statistical sampling, obviously. They’re not all spit out of a computer system. I think that requires more care and attention."
stats:prediction  stats:measurement  soc:quantification  data:availability 
august 2017 by phnk
How (and Why) We’re Collecting Cook County Jail Data - ProPublica
"The Chicago Data Cooperative, a coalition of local newsrooms and civic-data organizations, is starting to get detailed inmate data via Freedom of Information requests. But there’s even more information to get." -- Contains great insights about Scrapy, and good advice about scraping in general.
data:availability  violence:prison  usa:society  data:journalism  soc:quantification  python 
july 2017 by phnk
DATACTIVE – The Politics of Data According to Civil Society
"We are DATACTIVE, a research project and a research collective exploring the politics of big data broadly defined. We take a critical look at massive data collection, privacy and surveillance | social movements, activism and internet activism | internet infrastructure, cybersecurity and their governance | open data and civic tech networks."
soc:quantification  data:availability 
july 2017 by phnk
Towards a Genealogy of Open Data by Jonathan Gray :: SSRN
(Cautionary note: I have no idea whether any of those papers are any good.)
soc:quantification  data:availability 
july 2017 by phnk
Mary Douglas reviews ‘The Taming of Chance’ by Ian Hacking · LRB 23 May 1991
"Now we are so used to thinking statistically that we hardly notice how much we are besieged by politically serviceable numbers, averages and chances."
soc:quantification  stats:measurement  stats:probability 
july 2017 by phnk
Public Data Lab
"The Public Data Lab seeks to facilitate research, democratic engagement and public debate around the future of the data society."
soc:quantification  data:availability 
july 2017 by phnk
QuantGov: A New Home for Open Source Policy Analytics
"QuantGov is an effort to expand the frontiers of economic and legal research by providing an open-source framework to uncover the latent data in legal text. QuantGov grew out of the RegData project, which seeks to solve the longstanding problem of the quantification of federal regulation for use in economic analysis."
data:social-science  data:economics  soc:quantification 
july 2017 by phnk
Presidential Power Scores |
"The scores are based on research by David Doyle and Robert Elgie. The paper in which the research is fully presented is forthcoming in the British Journal of Political Science"
polisci:power  soc:quantification 
june 2017 by phnk
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