The number of research articles published each year grows exponentially. We often estimate the rate of growth to between 4% to 6% a year.
We are publishing a lot more in Computer Science. Editors must work a lot harder than they used to.
According to Sakr and Alomari, the size of the database research community doubled in the last ten years, and so did the number of papers they publish. The next table shows the number of papers accepted by VLDB (a major venue in database research) each year.
|VLDB proceedings||number of articles|
Journals also grow in popularity. For example, the Computer Journal has had to double the number of issues it publishes in less than 5 years.
|Computer Journal||number of issues|
Giving the rising costs of conferences and the cutbacks in research funding, we might even expect journals to grow faster than conferences.
Growth has superlinear effects: a system with twice as many variables isn’t merely twice as complex. Reviewers and editors have to work harder year after year even if their numbers increase. There is simply a higher coordination cost. And community sizes are limited by our cognitive abilities. This bound is called the Dunbar number. Despite what Facebook would have you believe, people cannot have many more than 150 acquaintances. So, if in the last 10 years, the number of researchers in your area has doubled, the number of researchers you can trust has probably remained the same.
In the past, this growth has lead to fragmentation. Scholars have become more narrow. But there is also a cost to this greater specialization: many of the most important problems require a broad expertise. Specialization often leads to irrelevance.
So far, we have kept disciplines from fragmenting by automating more and more of the peer review process. This trend is likely to continue. I believe that, one day soon, we will replace journal editors by robots. I think that Google is leading the way in this respect. For example, from my Google Scholar profile, Google recommends recent research papers to me. It is irrelevant where these papers appeared, as long as they are likely to be useful for my work. In some sense, we are bypassing human beings and scaling up with the help of computers.
The research enterprise of tomorrow will look a lot more like YouTube. You have millions of people crafting their content and hoping to attract some shred of attention. Much of the filtering and recommendation process is automated.
I am not claiming that relationships between researchers will become less important. In some sense, they will become even more important. In a world where you mostly interact with strangers you cannot trust, your trusted friends are key to preserving your sanity.
Further reading: The Future of Peer Review (via Venkat Rao)