Eva Vivalt

It’s been a while since that last post and a lot has changed in the interim.

I am pleased to announce I have taken up my position at the Research School of Economics at the Australian National University, after visiting Stanford. The position also carries with it the title of Inaugural Wealth and Wellbeing Fellow, and there’s no teaching until 2018.

The three research projects I have been working on that I am most excited about I unfortunately can’t talk about yet publicly. Hopefully, I will be able to resume blogging later in the fall or winter.

I recently participated in EAGxMelbourne and EA Global, as well as seminars at the University of Melbourne and UNSW.

As for future travel plans, I have some things scheduled in North America (Berkeley, NYC, Princeton, Chicago) and, later on, Hong Kong and Singapore. Let me know if you are nearby one of those areas and interested in meeting up.

I am glad to be part of the Social Science Meta-Analysis and Research Transparency (SSMART) review committee for its next round of projects. A total of $230,000 is available in grants of up to $30,000.

I am also excited to be on an oversight committee put together by ACE to assist their newly hired research officer Greg Boese in making decisions about which research to fund. TJ Mather and Maxmind have pledged $1,000,000 over the next three years to investigate the most effective ways to help animals, an area in which very little research exists. The people on the committee are very impressive and I look forward to working with them!

Research committees can be fun, because you get to stay apprised of all the new and exciting projects before they happen. They are also a great form of effective altruism; 80,000 Hours often recommends the somewhat similar work of a foundation grantmaker.

I am very excited to see what comes out of these initiatives.

AidGrade is starting to use machine learning to help extract data from academic papers for meta-analysis. This is a big deal – meta-analyses tend to go out of date quickly because data extraction is such a time-intensive process and new studies are constantly coming out at an ever-increasing rate.

AidGrade will use its existing database of impact evaluation results to help build and validate models. For each extracted piece of information, it will also generate a probability that the information is correct.

At the very minimum, this will reduce the amount of time it takes to identify key characteristics of studies, such as where they were done and which methods they used. It is also the only way to ensure that meta-analyses are perpetually updated as new studies come out. Given that the methods should be scalable to much of economics, education, and health (think of a ScienceScape (update: now known as Meta) for meta-analysis – they have catalogued 25 million studies, a number which one would definitely need machine learning to process!), it will build this tool in a general way so that its results can be used to inform policy even in developed countries.

To support this, AidGrade has a new crowdfunding campaign. Please share and contribute.