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The publish-or-perish culture is destroying the quality of academic research

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National Institute of Technology Karnataka · Posted
Structural Engineering Economics

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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22 Replies

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Asha Pillai Distinguished 880 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

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Ganesh Menon Growing 140 pts · 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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Preethi Anand Distinguished 690 pts · Accepted answer

+1 to everything said above. My experience was identical.

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Replying to Preethi Anand
Vikram Bhatia Active 420 pts · Accepted answer

+1 to everything said above. My experience was identical.

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Meena Sharma · 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 Meena Sharma
Lakshmi Devi Growing 75 pts · Accepted answer

When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?

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Fatima Sheikh Growing 95 pts · Accepted answer

Great question.I went through something very similar in my second year.

The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.

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Replying to Fatima Sheikh
Indira Balan · Accepted answer

CSIR-UGC NET preparation: past papers are the most important resource. Everything else is secondary.

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Replying to Fatima Sheikh
Rohan Desai Distinguished 950 pts · Accepted answer

Check whether your institution has a Springer/Elsevier waiver agreement.many NITs and IITs do.

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Replying to Fatima Sheikh
Karthik Rajan Growing 180 pts · Accepted answer

I found that writing a paper abstract first (before the paper) and then reverse-engineering the paper from the abstract helped with clarity enormously.

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Prakash Iyer Distinguished 640 pts · Accepted answer

Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?

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Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

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Bindiya Thomas Growing 65 pts · Accepted answer

When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?

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Admin NITK · Accepted answer

Just to make sure I understand the Zotero workflow correctly.is the .bib file synced to Overleaf automatically every time you add a new paper, or do you need to trigger an export manually?

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Replying to Admin NITK
Om Prakash Growing 55 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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Ashwin Murthy Distinguished 560 pts · Accepted answer

The ethics review situation in Indian universities is actually more complex than most people realize.

There is no uniform national framework equivalent to the US Common Rule. Each institution has its own Institutional Ethics Committee (IEC) or Institutional Review Board (IRB).

For anonymous survey research with no identifiable data and no vulnerable populations: most Indian IECs would classify this as exempt, but 'exempt' still needs a formal determination from the IEC.it is not self-certifying.

My practical advice: go to your institution's research ethics office now, describe what you did, and request an exempt determination in writing. Better to have documentation than to discover the issue during thesis review.

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Replying to Ashwin Murthy
Vikram Bhatia Active 420 pts · Accepted answer

This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.

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Replying to Ashwin Murthy
Lalitha Mohan Active 230 pts · Accepted answer

The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.

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Replying to Ashwin Murthy
Nandita Ghosh Distinguished 720 pts · Accepted answer

This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.

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Dinesh Kulkarni Distinguished 730 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.

0
Royal Dsouza Starting 30 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

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Admin NITK · Accepted answer

I'd push back slightly on the idea that industry collaboration is universally beneficial for academic research. The incentive misalignment is real.

Industry partners want deliverables on a schedule. Academic research is often exploratory and unpredictable. When the two clash, it is usually the researcher who has to compromise.either by rushing results, constraining publication, or steering away from results that are inconvenient for the sponsor.

This doesn't mean industry collaboration is bad. But the terms matter enormously. IP rights, publication rights, and the ability to pursue negative results should be negotiated upfront and protected in writing.