China social credit system: the method to reward or punish citizens for their behavior is unclear

First announced in 2014, the Chinese government has been implementing a controversial social credit system. The system uses secret algorithms to evaluate the behavior of Chinese citizens, monitor their banking and social media information such as communications on popular chat app WeChat, violation of traffic rules, and countless other factors and criteria that the government is reluctant to publicly disclose. One of the major concerns is the vague credit score criteria may have some negative impact on a business’s ability to do business in China.

Chinese citizens behaviors excessively tracked by the Chinese government are used to calculate a social score that determines whether you are a good Chinese citizen or not. According to China’s rather unclear standards, if you’re ranked as a good citizen with high credit scores, you get to enjoy priority in some national services such as public transport and quality education; if you’re ranked as a bad citizen with low credit scores, you risk losing access to a wide range of services such as traveling within or outside of China. In fact, just last year China banned people from purchasing airplane or train tickets 23 million times because their social credit scores were too low.

The problem with China’s social credit system is that the government who first introduced the social credit system wouldn’t tell you how the credit scores are being measured. In other words, you wouldn’t know if you’re doing the right thing or not. For instance, you won’t be able to know what are the things you do would get you rewarded, and what are the bad things that you do would get you punished. It is lack of transparency; a vague and unclear system that is highly controversial. Furthermore, you need to do a lot of reverse engineering in order to learn parts of the algorithms behind.

Evaluating academic journal value through objective metrics

In contrast to China’s secret metrics for assessing citizen behaviors, academic journal’s impact factor and other metrics for evaluating articles are more predictable and transparent in general. There are several statistical methods that can calculate the impact of a journal or an author. The most popular metrics to determine your academic success are number of citations (journal or author) and a journal’s impact factor. The Impact Factor is the most well-known, the most commonly used metric for evaluating scholarly articles. It’s the average number of citations received by articles in a journal within two years.

Journal impact factor is based on how often articles published in that journal during the previous two years were cited by articles published in a particular year. If you have a higher journal impact factor, the more frequently your articles in that journal are likely to get cited by other articles. The impact factor indicates how prestigious a journal is in its field.

Diode’s BlockQuick Reputation Table: Objective, Open, Transparent

BlockQuick, a super light client protocol for Ethereum, is using a reputation system that mathematically calculate a miner’s reputation based on previous mining power history. BlockQuick’s reputation system validates a block based on the BlockQuick Consensus Reputation Table. The calculating method is much like the journal impact factor, it’s entirely objective, open, transparent, and accountable. BlockQuick is not controlling the reputation itself; the reputation is autonomously calculated and based on the number of blocks generated by each miner. The reputation scores are objectively based on the actual mining power.

Find our more about the details of how BlockQuick’s consensus reputation table works in our previous blog post. The Proof of Concept (POC) code of BlockQuick will soon to be released on GitHub; you will be able to see how the reputation is being calculated. So stay connected! Follow us on Twitter and our website. Stay up-to-date and subscribe to our newsletter.