[UAI] letter of resignation from Machine Learning journal

From: Michael Jordan (jordan@cs.berkeley.edu)
Date: Mon Oct 08 2001 - 16:28:26 PDT

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    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



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