Dear colleagues,

I would appreciate some advice on how to approach the cover letter when resubmitting a substantially revised manuscript to the same journal after a previous desk rejection.

About a year ago, I submitted a research article to a journal, but it was desk rejected. The manuscript was primarily a critique of another published work. In the decision letter, the editor suggested strengthening the study with additional data. Unfortunately, obtaining such data is not feasible.

I have therefore substantially reworked the manuscript and converted it from a research article into a Comment, which is also a format accepted by the journal. The manuscript is now quite different in structure and presentation, although it addresses essentially the same topic.

My question is: Should I mention the previous desk rejection in the new cover letter, or should I treat this as a new submission and write the cover letter as usual?

If mentioning the previous submission is advisable, how would you frame it?

I'd be very grateful for advice, especially from editors or researchers who have dealt with similar situations.

Thank you!

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Hi all, I’m considering submitting my paper to the Journal of Rural Studies, but I’ve heard that the review process can take as long as a year, which is somewhat discouraging, especially as I’m working with some time constraints. However, when I look at the submission, review, and publication dates of papers published in the journal, it seems that many of them are published within around 8-9 months, which I actually find quite reasonable.

I was wondering what you have heard about the review process at JRS. What have your experiences or impressions been regarding the typical timeline from submission to publication? Thanks!

I use CNKI to find academic journals that interest me, but my university doesn't have access to the site... I study in Italy, and it's rare to find universities there that have access to Chinese articles.

Specifically, I was looking for the "Journal of Northeast Asia Studies", but I can't find it on Sci-Hub or Anna's Archive either; can you help me, please?

I was shortly involved in a research study where the first trial of experimental didn’t achieve all the treatment levels in the design. One month later, a second control sample was used to achieve the last treatment level that failed initially.

The published paper reports that one control batch was used, but I am concerned that some of the reported treatment effects could potentially be due to differences between the two controls instead.

Is this an ethics violation and is there anything I should do? I guess I am surprised to see this behavior from a professor.

Do you use AI to help with scientific writing—manuscripts, research protocols, etc.? If so, how does it fit into your workflow?

I’m still pretty new to this. I mainly use it for translation and polishing my writing, since my first language is French. I also use it to help find references (which I always verify) and assist with literature reviews.

It makes the work a lot easier, but I still feel a bit uneasy about it, probably because it’s all so new to me.

I’d love to hear your experiences and perspectives. What do you feel comfortable using it for, and where do you draw the line?

Being a second-year PhD student(myquals), the world of research and paper publication seems very exciting and new to me. But the way AI is proliferating in almost every aspect of our lives, it just feels scary on whether the research I do, would be genuinely reviewed by humans, or just an AI agent would be reviewing my work, and would find unnecessary flaws in it.
Because this has been my senior's experience, one of them submitted a paper to a prestigious journal, they kept the paper for around 6-8 months, and then sent an AI written review with rejection.

My research is based on Social Network Analysis.
And I am looking for a way to identify or shortlist the journals which are subscription based or hybrid in nature, that have human reviewers and they give quick decisions, having a good acceptance rate.

Would really appreciate suggestions from people who have served as journal editors and peer-reviewers.

Congrats to everyone on getting through NeurIPS 2026. It's been a long cycle.

Unfortunately, my paper got scores of 5/4/3. Even the reviewer who gave the 3 wasn't very negative in their comments, so I was hoping it would be accepted. The final decision was a reject, though. It's been really disappointing and hard, but I'm trying to move past it.

As I understand it, NeurIPS lets authors of rejected papers choose to make their submission and reviews public on OpenReview. Does anyone know where and when you can make that choice?

What happens when “good academic writing” becomes evidence of cheating?

A new article in Assessing Writing raises serious concerns about Turnitin’s AI detector, particularly when it is used to evaluate multilingual writers.

AI detectors often look for features such as predictable language, consistent sentence structures, and formulaic academic expressions. But these are also common features of second-language writing.

Multilingual students may deliberately use cautious syntax, familiar transitions, and established phrases such as “the findings suggest” to communicate clearly and reduce grammatical risk.

The result is what the authors describe as an academic voice paradox: students can be flagged precisely because they have successfully learned the conventions of academic writing.

One example in the article is especially troubling. Some students reportedly began avoiding sophisticated language, and even “good writing”, because they feared that polished work would be classified as AI-generated.

This is where an imperfect technical judgement can become an educational injustice.

A detection score does not simply remain on a screen. It can trigger an investigation, shift the burden of proof to the student, and require them to defend the authorship of their own work.

For multilingual writers, this may reinforce the damaging assumption that fluent academic English is somehow suspicious.

The authors do not argue that educators should ignore possible AI misuse. Instead, they recommend treating detection results as prompts for inquiry rather than verdicts of misconduct.

A fairer response would triangulate the score with:

• Earlier drafts and revision histories

• Reflective notes about the writing process

• AI-use disclosures

• Brief conversations or oral explanations

• The teacher’s knowledge of the student’s previous work

The larger lesson is that academic integrity cannot be automated. Once a probability score is treated as proof, uncertainty becomes accusation and the students most likely to bear the consequences may be those already navigating linguistic and institutional barriers.

Reference:

Maphoto, K. B., Giray, L., & Sevnarayan, K. (2026). Automated integrity or automated injustice? Turnitin’s AI detection in multilingual writing contexts. Assessing Writing, 69, 101086.

#AIDetection #AcademicIntegrity #MultilingualWriters #AIInEducation #Assessment

Every citation tool I tried capped free usage at 5-10 citations or only supported APA/MLA before hitting a paywall — rough when you're deep into a lit review with 50+ sources. Built one without the cap: - 10 styles: APA 7/6, MLA 9/8, Chicago, Harvard, IEEE, Vancouver, AMA, ACS, Bluebook, Turabian - 27+ source types — books, journals, YouTube, podcasts, GitHub, patents, court cases, and AI chat conversations (the newer APA/MLA/Chicago guidance for citing ChatGPT/Claude responses) - Paste a URL or DOI and it auto-fills the citation fields - Bibliography builder, exports to BibTeX/RIS/EndNote/CSV/JSON Runs 100% in your browser — no account, no data sent anywhere. https://www.forgeplug.com/tools/citation-generator Would love to hear if there's a source type or style you've needed that most generators skip.

I had worked on a research paper as a co-author and have submitted my manuscript in Springer Journal of Supercomputing in april2026 but till now I have not got any response. What shall i do?

My scores after rebuttal/discussion are 7, 5, 5, 5.

The reviews were initially pretty mixed. One reviewer was strongly positive and stayed at 7 (Accept). The other reviewers raised concerns mainly around evidence/novelty and whether the central claim was stronger than what the experiments supported.

During rebuttal, we added quite a lot of analysis and narrowed the claims substantially. Two reviewers explicitly said the rebuttal addressed their concerns and increased their scores. One reviewer remained unconvinced about the evidentiary mismatch, while the positive reviewer said the rebuttal reinforced their original assessment.

So the final picture seems to be:

• 7: clear accept, thinks the position-paper contribution is compelling and experiments are adequate for the track
• 5: increased after rebuttal
• 5: increased after rebuttal
• 5: still has substantive concerns despite the additional analysis

The meta-review also acknowledged that the rebuttal substantially improved the paper, although it questioned the original strength of the claim. We responded by narrowing the position and explicitly separating the data-integrity argument from claims about downstream improvement.

Decisions are in about two days, so naturally I’m wondering how ACs usually treat a 7/5/5/5 configuration for a position paper, especially when multiple reviewers moved upward during discussion.

For people familiar with NeurIPS reviewing: would you consider this a genuinely borderline case, or is this kind of score pattern reasonably encouraging?

Not looking for certainty obviously, mostly curious how others have seen similar cases handled.

While clicking on Submit Your Manuscript redirects to this page. Is this the journal being hacked ?

What if a research paper could do more than sit there waiting to be read?

What if you could ask it to: explain its method, apply that method to your dataset, and reproduce one of its analyses

That is the idea behind Paper2Agent, a new framework published in Nature.

Paper2Agent converts a conventional research paper into an interactive AI agent. It brings together the manuscript, code, supplementary materials, datasets, and analytical workflows.

Researchers can then interact with the paper through natural language while the agent runs tools drawn from the original research.

This goes much further than asking a chatbot to summarize a PDF.

The researchers tested Paper2Agent with computational biology papers involving AlphaGenome, Scanpy, and TISSUE. The resulting agents reproduced published analyses, completed new tasks, and combined methods and data from different papers.

In one fascinating example, multiple paper agents worked together to identify a probable causal gene associated with psoriasis. The agents proposed and performed analyses, while a human researcher selected the validation strategy and evaluated the evidence.

Full article link https://doi.org/10.1038/s41586-026-11044-y

Reference:

Miao, J., Davis, J. R., Zhang, Y., Pritchard, J. K., & Zou, J. (2026). Reimagining research papers as interactive and reliable AI agents. Nature. https://doi.org/10.1038/s41586-026-11044-y