“The recent “Landscape Analysis” from SPARC, released at the end of March, walks readers through a sober-sided evaluation of the market, with an emphasis on the major publishers — Elsevier, Wiley, and Springer Nature on the journals side, with Pearson and McGraw-Hill (and Cengage, now part of McGraw-Hill) on the education side.
There are some interesting analyses in the document, and some surprising figures to be sure (for example, Elsevier makes less money per article than either Springer Nature or Wiley). There are tables showing the asymmetry of usage (~5-7% of journals account for ~50% or more usage in multiple fields), but drawing what I consider to be unforgiving conclusion (I don’t agree that low usage equates to low value — a lot of good science and scholarship comes out of small disciplines, new ways of thinking, or emerging fields). The authors confirm in passing my finding that subscriptions only cost academic institutions 0.5% of their budgets. There are arguments about productivity gains, and some contradictory and incomplete data. But overall, the analysis seems solid at the detailed level, missing the mark only a few times here and there.
What’s truly interesting about the analysis is the forest it describes — the big picture it asserts — which is alive with customer knowledge and Big Data assumptions. The authors examine how Elsevier and other companies are now pivoting away from content and into the surveillance economy.
The implications of this are examined in the analysis through a narrow premise — that academic institutions can and should guide the data acquisition and analysis practices of private firms using information products as ways to ignite data exhaust they can use to sell information and projections about academic practices, research areas, and individuals back to institutions.
What the analysis describes is a fascinating — and totally expected — pivot, one we’ve seen developing for quite some time. The SPARC analysis puts a pin in it, and states it quite explicitly.
But exploring the forest is where the analysis falls down, failing multiple times to answer questions its own premise begs — for instance, it asserts data acquisition and analysis should be guided by academic culture, without testing whether there is actually something we can identify as “academic culture” against which proper data utilization practices can be judged….”