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How to handle authorship disputes in collaborative research.share your experience (55)

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Starting 0 pts 0 followers
Bearys Institute of Technology · Posted
Curriculum & Pedagogy

I want to start an honest conversation about something I have been thinking about for a while.

As a fourth-year PhD student, I have watched the academic publication ecosystem up close. The pressure to publish in high-impact journals has created perverse incentives:

  • Researchers cherry-pick results to show positive findings
  • Null results are systematically rejected
  • The same idea gets cut into multiple 'minimum publishable units' to inflate output
  • Citation rings inflate impact scores

What do you think? Is this a structural problem that needs policy-level intervention, or are there practical things individual researchers can do?

I am particularly interested in hearing from senior faculty who have seen this evolve over their careers.

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Hema Suresh Starting 20 pts · Accepted answer

The short answer: it depends heavily on the journal and field, but here is a general framework.

If your null result is well-powered and the hypothesis was reasonable: Absolutely publish it. Null results in well-designed studies are as valuable as positive results. Journals like PLOS ONE explicitly welcome them.

If your study is underpowered: Be transparent about it. Report effect sizes and confidence intervals, not just p-values. A small, underpowered study with honest reporting is still a contribution.

What kills papers is when limitations are obvious but the authors have not addressed them.

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Dinesh Kulkarni Distinguished 730 pts · Accepted answer

Thank you for this detailed answer! A quick follow-up: when you mention cosine learning rate with warm-up, are you warming up the LoRA parameters specifically or the entire model including the frozen backbone?

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Lakshmi Devi Growing 75 pts · Accepted answer

As a senior faculty member (25 years in the system), I want to offer a different perspective. Yes, the pressures are real. But I have also seen the other side: students who produce excellent work, publish honestly, and build reputations over time. The system is imperfect but not entirely broken.

What has actually helped my students: choosing venues carefully (a few strong papers rather than many weak ones), developing genuine collaborations rather than transactional coauthorships.

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Replying to Lakshmi Devi
Arjun Venkatesan Growing 195 pts · Accepted answer

Strongly agree with the point about choosing venues carefully. One paper in NeurIPS > five papers in obscure workshops.

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Replying to Lakshmi Devi
Tara Singh Starting 5 pts · Accepted answer

Marking this as the accepted answer. Incredibly helpful and specific. Really appreciate you taking the time.

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Farhan Mirza · Accepted answer

I think you are conflating two separate problems here. The publish-or-perish culture creates pressure, yes.but the solution is not to criticize researchers who are navigating an unfair system. The real problem is with evaluation committees who treat publication count as a proxy for research quality. Fix the evaluation, and the incentives change.

Also worth noting: open review has been implemented in some fields and has its own problems. It can disadvantage early-career researchers who fear retaliation from senior colleagues they have reviewed critically.

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Dinesh Kulkarni Distinguished 730 pts · Accepted answer

For Zotero vs BibTeX management, I moved to Zotero + Better BibTeX 3 years ago and have not looked back.

My workflow:

  1. Zotero browser connector captures papers instantly from ArXiv, Google Scholar, ACM DL, IEEE Xplore
  2. Better BibTeX generates a clean .bib file that auto-updates whenever I add a paper
  3. Overleaf directly pulls from the .bib file via Dropbox sync

One tip: create a Zotero collection per paper/project. When you export the .bib, export only that collection to keep it clean.

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Karthik Rajan Growing 180 pts · Accepted answer

I have a slightly different take from my experience in industry research. The reproducibility crisis is real but unevenly distributed. Fields with strong engineering culture (computational biology, ML with benchmarks) have actually improved significantly in the last 5 years. The bigger problem is in fields where data sharing is structurally difficult.clinical medicine, behavioral economics.

The ML community's move toward open code and reproducibility checklists has been genuinely effective.