[Preprint] State power and elite autonomy: The board interlock network of Chinese non-profits

Ji Ma, Simon DeDeo

In response to failures of central planning, the Chinese government has experimented not only with free-market trade zones, but with allowing non-profit foundations to operate in a decentralized fashion. A network study shows how these foundations have connected together by sharing board members, in a structural parallel to what is seen in corporations in the United States. This board interlock leads to the emergence of an elite group with privileged network positions. While the presence of government officials on non-profit boards is widespread, state officials are much less common in a subgroup of foundations that control just over half of all revenue in the network. This subgroup, associated with business elites, not only enjoys higher levels of within-elite links, but even preferentially excludes government officials from the nodes with higher degree. The emergence of this structurally autonomous sphere is associated with major political and social events in the state-society relationship.

For full text, refer to http://arxiv.org/abs/1606.08103

Parallel computing using IPython: Important notes for naive scholars without CS background

Analysis of network and complex system requires too much computing resources. Although the learning curve is deep, the power of parallel computing must be utilized, otherwise, more time will be spent on waiting. Moreover, for exploratory academic research, we will not know what’s the next step until we finish the current analysis. So the research life-cycle becomes hypothesis -> operationalization -> LONG TIME coding and debugging -> LONG TIME waiting for result -> new hypothesis.

With IPython Notebook, parallel computing can be easily operated; however, like what I’ve said: We cannot understand the easiest programming skills unless we are able to operate them. I’ll not come to this post if I do not have to wait for a week only for one result. Playing parallel computing with IPython is easy, but for real jobs, it’s not. Scholars in social science area may be less skilled in programming – we are not trained to be. I’ve made great efforts and finally got some progress which may be laughed by CS guys.

While using IPython Notebook (now named Jupyter Notebook) for parallel computing, Jupyter will start several remote engines beside the local one we are using. These remote engines are blank which means that the variables and functions defined and modules imported on the local engine do not work on the remote ones. Specifically, the puzzle for me was (yes, was!): How to operate the variables, functions, and modules on the remote engines.

Continue reading “Parallel computing using IPython: Important notes for naive scholars without CS background”

Extracting information from text: Using Python NLTK library and TF-IDF algorithm

Update: NLTK has its own algorithm for TF-IDF, please forgive my ignorance.

http://www.nltk.org/_modules/nltk/text.html#TextCollection.tf_idf

=====================

I’ve been working on extracting information from a large amount of text which mainly consists of thousands of journal article’s abstract. TF–IDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. Wikipedia has a page introducing this algorithm in detail, so I will not discuss more about this.

Continue reading “Extracting information from text: Using Python NLTK library and TF-IDF algorithm”

Understanding the Journal Review Process: How Associate Editors Work?

I have submitted a manuscript in mid-January; thereafter, I got another routine besides refreshing my Facebook page. The progress has been staying in “Awaiting Referee Selection” for about two months; until today, it changes to “Awaiting Referee Invitation.” I am so curious (and also frustrated) about the review process, and the following slide meets my curiosity perfectly – it will tell you how Associate Editors work.

This is an operation manual of Manuscript Central for AEs. MC is a popular manuscript processing system through which I have submitted my paper. I have embedded this file in this post, original link of this file is:

http://secure.oxfordjournals.org/our_journals/jjco/aemanualeng.ppt

[gview file=”http://maji.tacc.utexas.edu/wp-content/uploads/2015/03/aemanualeng.ppt”]

A simple data visualization example: Project Database for Chinese Offset Projects

Data visualization is one of my main focuses this semester. I have been trying several different visualization tools, such as python’s matplotlib, VTK, Plotly, and finally I came to Tableau. Online and interaction are two important characters, especially for the visualization of information in huge size (which we may call big data). Tableau is the best one that can perfectly meet my demands (so far): you don’t have to coding, totally GUI interface but still retains great flexibility, online and interactive. The following chart is a simple example.

[cjtoolbox name=’Project Database for Chinese Offset Projects’]