Letters

SIGIR Forum 35(2)

 

 

Editorial Board of the Kluwer Journal, Machine Learning: Resignation Letter

 

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 centres 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 http://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


 


The Kluwer Journal, Machine Learning: Access Policies

 

Dear colleagues,

 

In response to the widely circulated letter of resignation of some members of the Machine Learning journal (MLJ), I would like to make two points:

 

The accessibility of MLJ papers has been dramatically improved in the past 12 months. The main changes are these:

        the copyright agreement gives the author the right to distribute individual copies of an MLJ paper to students and colleagues, physically and electronically, including making the paper available from the author's personal web site.

        the individual MLJ subscription price has been dramatically reduced. It is excellent value for money: for $120 Kluwer prints, binds, and mails to your door around 1350 pages.

 

As a consequence of the first two points, MLJ articles are universally accessible -- from Kluwer's home page in the first six months or so, and at any time from the author's home page.

 

The primary purpose of paid subscriptions, in this new distribution model, is to enable an individual or institution to obtain a bound archival copy of the journal printed on high-quality paper -- exactly the same role served by the printed version of JMLR sold by MIT Press.

 

Turning to the second point, all members of both editorial boards have the interests of the machine learning community at heart. Our job is to serve you.

 

The current members of the MLJ board, and the new members we are in the process of adding, believe it is in the best interests of the research community to keep MLJ alive and strong at this time. This is not to say we hope JMLR will fail. There is ample excellent research to support two high-quality journals, so it is not necessary for one of the journals to be destroyed in order for the other to succeed.

 

If you agree that MLJ is useful to the community and has a role to play in the future, I would like to hear from you - feedback from the community is the very best way for me to know how to steer MLJ's course so it best serves the community.

 

Robert Holte holte@cs.ualberta.ca

Executive EditorMachine Learning

 

 

 


On Less Restrictive Access to Archival Research Literature

 

To the Information Retrieval Community:

 

A letter was recently distributed by Dr. Michael Jordan of UC Berkeley and 39 other prominent researchers who resigned from the Editorial Board of the Kluwer journal, Machine Learning. The letter, which argues for less restrictive access to the archival research literature, can be found in a recent issue of SIG-IRList

(http://www.acm.org/sigir/sigirlist/) and also at

http://www.cs.orst.edu/~dambrosi/uai-archive/0822.html.

 

This event is of interest to the information retrieval community, given that an attempt has been underway for several years to establish another Kluwer journal, Information Retrieval, as the core journal in IR. Indeed, I participated in that attempt in its early years, recruiting a number of the members of the Editorial Board. It is important to appreciate the importance to our field of having a journal focused purely on IR, and it is important to acknowledge the contribution of Paul Kantor, Steve Robertson, the Editorial Board, and the authors of papers in establishing this journal.

 

Over the years, however, my sentiments have come to largely reflect those of those who recently left Machine Learning. In July 2001, I resigned from the Editorial Board of Information Retrieval, and of two other journals without open archives. The World Wide Web allows broader distribution of research results than at any previous time in history, but only if changes are made in how academic publishing works.

 

It is important to acknowledge that many things are unclear about how academic publishing will evolve. How to reconcile open archives with the financial needs of professional organizations such as ACM is a particularly difficult question. The role of conference proceedings, with their quasi-archival status, is also unclear. These issues are being discussed in a variety of forums, including at:

http://www.nature.com/nature/debates/e-access/

 

Would an open archive, noncommercial, journal like JMLR (http://www.jmlr.org/) or JAIR (http://www.jair.org/) be viable in information retrieval? I believe it would, but to forestall the obvious question, I have no plans to found such a journal myself, or to participate more in this debate than the hour spent writing this letter. My new career as a self-supporting consultant doesn't give me that luxury.

 

What I can do is what each of us does: choose where to put the time I can afford to volunteer. I now serve only on the Editorial Board of JAIR, and I accept reviewing requests only from journals with open archives. If an IR journal with open archives were to be founded, I would do my best to support it. Further, I will publish journal articles on which I am the sole author only in journals with open archives.

 

Regards,

David D. Lewis

openarchemail@daviddlewis.com

Independent Consultant