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I had worked on a computer vision problem three years ago, drafted a journal paper and submitted it to a top-tier journal. My rebuttal to the reviewers answered most of their concerns except "more results required" comment, so got rejected. By then I had changed jobs, but wanted to get this paper published, so quickly made some changes and submitted to another journal, after two rounds of to-fro with the reviewers, the editor rejected it saying, "come back with more results". I don't have access to data as I have changed jobs, so "more results" is out of question.

I am now toying with the idea of getting this paper published in an open-access journal (where I believe, my chances of getting it published is high) or Arvix.org.

Should I go for a low-quality open-access journal or arXiv.org? I want my work to be out there. It may not be award-winning work but it's research and I want it out there.

How does publishing in a low-quality open-access journal (or arXiv) affect a candidate's chances when applying for a job? I know some people who hire use a point system i.e. A-grade journal = 5 points, etc. Do they have a "negative point system" for low-quality open-access journals?

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    "Open access" and "low quality" are orthogonal. – JeffE Dec 15 '13 at 12:46
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    @JeffE Sure? I observe at least some correlation: ijorcs.org – Dirk Dec 15 '13 at 21:33
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    @Dirk "orthogonal" might be a too strong hyporbola by Jeff. Still, it's true that most of the open-access journals show good quality of papers. On one hand they might not do so well in the quantitative criteria such as IF or similar, because if you're open-access, you don't have the money to push yourself up in the rankings. On the other hand, many open-access journals are recognized as high-quality simply because high-level researchers publish in them. – yo' Dec 15 '13 at 21:48
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    might be a too strong hyperbole — Well...maybe. Open-access papers do tend to be cited more often, so perhaps there is some slight positive correlation between open-access and quality. – JeffE Dec 16 '13 at 2:42
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    @MatthewG. Conference registration costs around $1000. With low-quality open-access journal, I could probably get away with $200-300. I have to pay this out of my pocket as my previous affiliation will not bear the cost! – user2979010 Dec 16 '13 at 11:33
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First: Put your paper on the arxiv. It's a preprint server. Then the work is "out there" and you are still free to submit it to any reasonable journal (as noted in the comments, not every journal takes papers that are already on a preprint server, so check that in advance). So my short answer is: do both.

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    This is not necessarily true. Some journals do NOT allow publication on a preprint server. Check before you submit! – aeismail Dec 15 '13 at 21:45
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    Maybe true - (however, any journal I know do accept papers from the arxiv, sometimes you only need to send the arxiv number to do the submission). – Dirk Dec 15 '13 at 21:49
  • "Any journal I know" does not equal "any journal." Some of my papers (which are computational physics, and therefore discipline-relevant to arXiV) cannot be uploaded if I want to submit to some journals. Again, that's why it's important to check explicitly if this is OK. – aeismail Dec 15 '13 at 22:54
  • As far as I know, all computer science journals accept submissions that have been previously posted to ArXiv. – JeffE Dec 16 '13 at 2:45
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    @user2979010 why wouldn't you want to do both? The more people read your paper the better. – Marc Claesen Apr 28 '14 at 8:10
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Are your results only reproducible on one specific data set (the one you no longer have access to)?

If "more results required" is the comment you got from two different journals, I suggest you try to provide exactly that. Of course it's understood that having access to a suitable data is not always easy, yet often a requirement. Hence I think it would be worthwhile if you could try to get your hands on a different data collection on which you can perform further experiments. Also, if you repeat the ones you already did on the first data set, this can only strengthen your results.

This may be a lot of work, but remember, if your findings cannot be applied to anything but a data set that almost nobody has access to (including you), then there's little value to your results.

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