Discussion Solved

The case for open-access publishing: why paywalls hurt Indian researchers most (80)

Back to Forum
Growing 85 pts 0 followers
St Aloysius (Deemed to be University) · Posted
Sociology & Anthropology Finance & Banking Political Science & Governance

I have been on both sides.as a reviewer for IEEE and ACM journals, and as an author who has received peer review. The experience has made me wonder whether the current closed peer review model is serving science well.

As a reviewer: I put significant effort into reviews (usually 1500–2500 words). But I occasionally see reviews from other reviewers that are 3 sentences and completely uninformative.

As an author: I have received reviews that clearly misunderstood the paper's contribution. I have also received brilliant reviews that genuinely improved the paper.

Discussion question: What changes to peer review would actually improve quality? Open peer review? Paid reviewing? Double-blind everywhere?

Interested to hear from people across different fields and career stages.

Sign in to interact

Sign in to join the discussion.

80 Replies

Oldest Top
0
Yogesh Pandey Growing 70 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

0
Hemant Patwa · 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?

0
Bindiya Thomas Growing 65 pts · Accepted answer

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

0
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Replying to Deepa Krishnamurthy
Prakash Iyer Distinguished 640 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Replying to Deepa Krishnamurthy
Ishaan Verma Growing 60 pts · Accepted answer

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

0
Replying to Deepa Krishnamurthy
Bhavana Reddy Starting 30 pts · Accepted answer

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

0
Yogesh Pandey Growing 70 pts · Accepted answer

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

0
Sowmya Narayanan Active 310 pts · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.

0
Prakash Iyer Distinguished 640 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Replying to Prakash Iyer
Divya Krishnan Growing 85 pts · Accepted answer

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

0
Replying to Prakash Iyer
Disha Malhotra Starting 8 pts · Accepted answer

I am in a very similar situation. Would you be willing to share the outline of your PMRF proposal? Not the content.just the section headings and approximate word allocation.

0
Preethi Anand Distinguished 690 pts · Accepted answer

This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?

0
Amit Joshi Starting 15 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Meena Sharma · Accepted answer

I have successfully received PMRF (lateral entry, second year). Happy to share what worked in my proposal.

On the research proposal:

  • 60–70% technical approach, 30% context and impact
  • Preliminary results absolutely help. Coursework projects are fine if genuinely relevant
  • The societal impact section should be substantive.PMRF is a national initiative; reviewers want to see why this matters

On citations: Yes, cite your MTech thesis. Framing your PhD as an extension of prior work you did shows continuity of thought.

0
Replying to Meena Sharma
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.

0
Replying to Meena Sharma
Sanjay Kumar Growing 145 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?

0
Deepa Krishnamurthy Distinguished 810 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.

0
Replying to Deepa Krishnamurthy
Kiran Yadav Growing 130 pts · Accepted answer

This is very reassuring. I was worried the null result would kill the paper but your framing around transparency and effect sizes makes a lot of sense.

0
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

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

0
Jagdish Rawat · 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?

0
Ashwin Murthy Distinguished 560 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

0
Ekta Choudhary Starting 45 pts · Accepted answer

I am in a very similar situation. Would you be willing to share the outline of your PMRF proposal? Not the content.just the section headings and approximate word allocation.

0
Replying to Ekta Choudhary
Fatima Sheikh Growing 95 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.

0
Replying to Ekta Choudhary
Eshan Patil Growing 70 pts · Accepted answer

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

0
Replying to Ekta Choudhary
Fatima Sheikh Growing 95 pts · Accepted answer

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

0
Ravi Patel Starting 25 pts · Accepted answer

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

0
Rekha Shetty Starting 40 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?

0
Sunil Bhattacharya Active 390 pts · Accepted answer

Respectfully, I think this framing misses the systemic dimension. Individual researchers cannot solve a collective action problem. If you submit to open-access journals and your colleague submits to Nature, your colleague gets the promotion. Until evaluation criteria change at the institutional level.which requires policy intervention.individual choices have minimal impact.

0
Replying to Sunil Bhattacharya
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.

0
Admin NITK · Accepted answer

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

0
Ashwin Murthy Distinguished 560 pts · Accepted answer

Respectfully, I think this framing misses the systemic dimension. Individual researchers cannot solve a collective action problem. If you submit to open-access journals and your colleague submits to Nature, your colleague gets the promotion. Until evaluation criteria change at the institutional level.which requires policy intervention.individual choices have minimal impact.

0
Dinesh Kulkarni Distinguished 730 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Ganesh Menon Growing 140 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
Replying to Ganesh Menon
Chetan Jain Growing 100 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

0
Disha Malhotra Starting 8 pts · Accepted answer

Flower (flwr) is the most actively maintained and research-friendly federated learning framework right now. My group has been using it for 18 months on a healthcare project with 4 hospital sites.

Strengths:

  • Clean simulation API that lets you test on a single machine before deploying
  • Good support for custom aggregation strategies (FedProx, FedNova)
  • Active Discord community with quick responses from the maintainers

For non-IID data specifically, look at the FedProx strategy.it handles heterogeneous data distributions much better than FedAvg.

0
test tester Growing 165 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.

0
Replying to test tester
Rekha Shetty Starting 40 pts · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.

0
Replying to test tester
Suresh Iyer · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Vikram Bhatia Active 420 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.

0
Ashwin Murthy Distinguished 560 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

0
Ekta Choudhary Starting 45 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.

0
Ishaan Verma Growing 60 pts · Accepted answer

Respectfully, I think this framing misses the systemic dimension. Individual researchers cannot solve a collective action problem. If you submit to open-access journals and your colleague submits to Nature, your colleague gets the promotion. Until evaluation criteria change at the institutional level.which requires policy intervention.individual choices have minimal impact.

0
Replying to Ishaan Verma
Hema Suresh Starting 20 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?

0
Replying to Ishaan Verma
Suresh Iyer · Accepted answer

Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.

0
Replying to Ishaan Verma
Quamar Ahmed Growing 65 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Gopal Naidu · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Admin NITK · Accepted answer

I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.

For Q1 IEEE journals, expect:

  • Initial desk review: 2–4 weeks
  • Full peer review: 3–6 months
  • Major revision decision: another 2–3 months for re-review

As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.

0
Revathi Chandrasekaran Active 410 pts · Accepted answer

I am in a very similar situation. Would you be willing to share the outline of your PMRF proposal? Not the content.just the section headings and approximate word allocation.

0
Arjun Venkatesan Growing 195 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.

0
Jaya Lakshmanan Growing 50 pts · Accepted answer

This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?

0
Uday Bose Starting 5 pts · Accepted answer

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

0
Arjun Venkatesan Growing 195 pts · Accepted answer

Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.

0
Royal Dsouza Starting 30 pts · Accepted answer

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

0
Wajid Khan Growing 110 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?

0
Nandita Ghosh Distinguished 720 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.

0
Replying to Nandita Ghosh
Quamar Ahmed Growing 65 pts · Accepted answer

This is very reassuring. I was worried the null result would kill the paper but your framing around transparency and effect sizes makes a lot of sense.

0
Replying to Nandita Ghosh
Royal Dsouza Starting 30 pts · Accepted answer

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

0
Aditya Sharma Starting 45 pts · Accepted answer

Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.

0
Replying to Aditya Sharma
Geeta Rao · Accepted answer

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

0
Replying to Aditya Sharma
Farhan Mirza · Accepted answer

Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.

0
Zara Hussain Growing 55 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.

0
Sowmya Narayanan Active 310 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.

0
Kiran Yadav Growing 130 pts · Accepted answer

I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.

For Q1 IEEE journals, expect:

  • Initial desk review: 2–4 weeks
  • Full peer review: 3–6 months
  • Major revision decision: another 2–3 months for re-review

As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.

0
Wajid Khan Growing 110 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Replying to Wajid Khan
Hemant Patwa · Accepted answer

Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.

0
Ishaan Verma Growing 60 pts · Accepted answer

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

0
Replying to Ishaan Verma
Nandita Ghosh Distinguished 720 pts · Accepted answer

If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.

0
Replying to Ishaan Verma
Rajesh Kumar · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

0
Replying to Ishaan Verma
Disha Malhotra Starting 8 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Eshan Patil Growing 70 pts · Accepted answer

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

0
Shiva Prasad Active 480 pts · Accepted answer

I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.

For Q1 IEEE journals, expect:

  • Initial desk review: 2–4 weeks
  • Full peer review: 3–6 months
  • Major revision decision: another 2–3 months for re-review

As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.

0
Neha Agarwal Growing 90 pts · Accepted answer

Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.

0
Chirag Mehta Starting 15 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

0
Shiva Prasad Active 480 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.

0
Replying to Shiva Prasad
Nandita Ghosh Distinguished 720 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?

0
Neha Agarwal Growing 90 pts · Accepted answer

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

0
Lalitha Mohan Active 230 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Pooja Nambiar Growing 115 pts · Accepted answer

If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.

0
Admin NITK · Accepted answer

If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.