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Cost Effectiveness for Open Access Journals

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Article Processing Charges – APCs, are one of the most widely discussed topics related to publishing in Open Access. The questions that are frequently raised are those of cost transfer (from the reader to the author) and, in particular, the effects of high-level fees on the impact of articles published in open access.

As one might expect, high APCs are often seen as an indicator of an OA journal’s prestige. Contrary to such a natural, one might think, ‘supply-demand’ assumption, the two factors do not always correlate in any significant way. The level of APCs does not always reflect the high impact of specific articles.

The presence of such weak correlations is well illustrated by a very interesting tool called Cost Effectiveness Open Access Journals, which compares the level of fees with Article Influence indicator that is based on citations. While one can question whether citations should be considered as the most important measure of influence of an article, citations are undoubtedly an important marker for authors and readers alike. The tool analyses a sample of 657 OA journals indexed by Thomson Reuters, but does not cover all available titles and all disciplines. The results of this analysis are, nevertheless, very interesting.

It turns out that the high fees charged by journals to publish articles do not necessarily translate to greater citation numbers – which in turn, would allow one to conclude, and well remember, that prices charged are not a good indication of a journal’s prestige. Moreover, most of the journals in the study that have published articles with high AI, set their APCs below $ 2,000 (and many of them do not charge at all); whereas, for example, the Journal of Physical Therapy Science with high APCs of $ 4,000, has a low AI score of 0.071.

It is of course difficult to build the prestige of a journal on citations alone. Other factors come into play and often do determine the actual choice of titles by scientists. Nonetheless, the new tool can help authors to choose an OA journal that will provide the best level of AI at the lowest cost, and thus can be of practical use. It should also be noted, that although high APCs do not always correlate with high AI, such a comparison tool could still, as noted by Peter Suber, stimulate price competition among publishers. I hope that the project will still develop further, and will increase the sample-base of analysed journals, as well as introduce additional factors.

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