[802SEC] FW: [UAI] letter of resignation from Machine Learning journal (fwd)
Dear Colleagues:
This is forwarded for your information. I urge you to consider and promote
the JMLR model as an intellectual property approach for the IEEE-SA, the
IEEE, and all its affiliated societies. What makes this so attractive?
-- rapid on-line availability to everyone at no cost
-- timely hard copy available for those who need it
-- IEEE entities retain sponsorship credit, quality control, and resulting
industry leadership image
-- IEEE continues to be the "first print publisher"
-- authors (the entire committee voting membership, individual and
institutional) retain rights to their work
The JMLR model, or something like it, is the way of the future for all
technical literature. Failure by the IEEE (and ACM, and ...) to do
something similar will likely result in a repetition of the scenario below.
Regards,
John Montague
---------- Forwarded message ----------
Date: Mon, 08 Oct 2001 16:28:26 -0700
From: Michael Jordan <jordan@cs.berkeley.edu>
To: "uai@cs.orst.edu" <uai@cs.orst.edu>
Subject: [UAI] letter of resignation from Machine Learning journal
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