of possible interest: ================================ Subject: [UAI] letter of resignation from Machine Learning journal Date: Mon, 08 Oct 2001 16:28:26 -0700 From: Michael Jordan <jordan@EECS.Berkeley.EDU> To: uai@cs.orst.edu Dear colleagues in machine learning, The forty people whose names appear below have resigned from the Editorial Board of the Machine Learning Journal (MLJ). We would like to make our resignations public, to explain the rationale for our action, and to indicate some of the implications that we see for members of the machine learning community worldwide. The machine learning community has come of age during a period of enormous change in the way that research publications are circulated. Fifteen years ago research papers did not circulate easily, and as with other research communities we were fortunate that a viable commercial publishing model was in place so that the fledgling MLJ could begin to circulate. The needs of the community, principally those of seeing our published papers circulate as widely and rapidly as possible, and the business model of commercial publishers were in harmony. Times have changed. Articles now circulate easily via the Internet, but unfortunately MLJ publications are under restricted access. Universities and research centers can pay a yearly fee of $1050 US to obtain unrestricted access to MLJ articles (and individuals can pay $120 US). While these fees provide access for institutions and individuals who can afford them, we feel that they also have the effect of limiting contact between the current machine learning community and the potentially much larger community of researchers worldwide whose participation in our field should be the fruit of the modern Internet. None of the revenue stream from the journal makes its way back to authors, and in this context authors should expect a particularly favorable return on their intellectual contribution---they should expect a service that maximizes the distribution of their work. We see little benefit accruing to our community from a mechanism that ensures revenue for a third party by restricting the communication channel between authors and readers. In the spring of 2000, a new journal, the Journal of Machine Learning Research (JMLR), was created, based on a new vision of the journal publication process in which the editorial board and authors retain significant control over the journal's content and distribution. Articles published in JMLR are available freely, without limits and without conditions, at the journal's website, http://www.jmlr.org. The content and format of the website are entirely controlled by the editorial board, which also serves its traditional function of ensuring rigorous peer review of journal articles. Finally, the journal is also published in a hardcopy version by MIT Press. Authors retain the copyright for the articles that they publish in JMLR. The following paragraph is taken from the agreement that every author signs with JMLR (see www.jmlr.org/forms/agreement.pdf): You [the author] retain copyright to your article, subject only to the specific rights given to MIT Press and to the Sponsor [the editorial board] in the following paragraphs. By retaining your copyright, you are reserving for yourself among other things unlimited rights of electronic distribution, and the right to license your work to other publishers, once the article has been published in JMLR by MIT Press and the Sponsor [the editorial board]. After first publication, your only obligation is to ensure that appropriate first publication credit is given to JMLR and MIT Press. We think that many will agree that this is an agreement that is reflective of the modern Internet, and is appealing in its recognition of the rights of authors to distribute their work as widely as possible. In particular, authors can leave copies of their JMLR articles on their own homepage. Over the years the editorial board of MLJ has expanded to encompass all of the various perspectives on the machine learning field, and the editorial board's efforts in this regard have contributed greatly to the sense of intellectual unity and community that many of us feel. We believe, however, that there is much more to achieve, and that our further growth and further impact will be enormously enhanced if via our flagship journal we are able to communicate more freely, easily, and universally. Our action is not unprecedented. As documented at the Scholarly Publishing and Academic Resources Coalition (SPARC) website, http://www.arl.org/sparc, there are many areas in science where researchers are moving to low-cost publication alternatives. One salient example is the case of the journal "Logic Programming". In 1999, the editors and editorial advisors of this journal resigned to join "Theory and Practice of Logic Programming", a Cambridge University Press journal that encourages electronic dissemination of papers. In summary, our resignation from the editorial board of MLJ reflects our belief that journals should principally serve the needs of the intellectual community, in particular by providing the immediate and universal access to journal articles that modern technology supports, and doing so at a cost that excludes no one. We are excited about JMLR, which provides this access and does so unconditionally. We feel that JMLR provides an ideal vehicle to support the near-term and long-term evolution of the field of machine learning and to serve as the flagship journal for the field. We invite all of the members of the community to submit their articles to the journal and to contribute actively to its growth. Sincerely yours, Chris Atkeson Peter Bartlett Andrew Barto Jonathan Baxter Yoshua Bengio Kristin Bennett Chris Bishop Justin Boyan Carla Brodley Claire Cardie William Cohen Peter Dayan Tom Dietterich Jerome Friedman Nir Friedman Zoubin Ghahramani David Heckerman Geoffrey Hinton Haym Hirsh Tommi Jaakkola Michael Jordan Leslie Kaelbling Daphne Koller John Lafferty Sridhar Mahadevan Marina Meila Andrew McCallum Tom Mitchell Stuart Russell Lawrence Saul Bernhard Schoelkopf John Shawe-Taylor Yoram Singer Satinder Singh Padhraic Smyth Richard Sutton Sebastian Thrun Manfred Warmuth Chris Williams Robert Williamson