Academics love to moan about publishing, but for good reason. If you're not in academia, please read on and marvel at how we supposedly bright sparks have been sucked into one of the most bizarre economic systems of the modern world.

Take Elsevier, one of the biggest publishers with many journals across disciplines. Its parent company, RELX, reported £1.035 billion in adjusted operating profit in 2025, on £2.714bn revenue: an insane margin of about 38%. RELX’s largest disclosed shareholders are BlackRock (9.67%) and Invesco (c.5%); not companies we typically associate with the pursuit of knowledge and truth.

Those margins are only achievable because most of the underlying labour comes from academia. Researchers conduct the research, largely funded by universities, governments and charities. We write the papers. Other academics peer-review them, generally for free (peer review is vital for good science - unbiased, constructive feedback). Academics also provide much of the editorial expertise, often unpaid or modestly remunerated. After all that, publishing a paper still takes many months (often over a year). Publishers provide infrastructure, production, hosting and editorial systems.

THEN - here's the really good part - universities BUY access to that literature back, or pay article-processing charges so everyone else can read it. For Elsevier alone, most UK universities spend hundreds of thousands of pounds a year; large, research-intensive institutions often spend £1m+. This against a backdrop of 35.8% of English higher-education providers reporting a deficit and 15,000 job cuts in a year - University and College Union (UCU).

So academics simultaneously supply much of the product, much of the quality control, and are the customer. Cool.

In a fun plot twist, scholarly content and data are becoming valuable inputs for AI (of course). Major publishers are licensing content/data for AI applications and building new, paid AI products around scholarly literature. Good old AI is also being used to write articles (complete with AI hallucinations and outright plagiarism) - making even more work for those unpaid editors and reviewers.

Surely there is a way for universities to use their existing infrastructure and keep more of the money that currently leaves the sector inside research itself?! Other models include university and learned-society publishing, diamond open access where neither reader nor author pays, consortial funding, Subscribe-to-Open, institutional repositories, and open science platforms. It takes a brave researcher, especially in the early years, to ditch the prestige that comes with a big name 'high impact' journal...and a lot of research to find these ethical routes.

Hit me up with viable solutions, or publishers you trust.

Also, are there industries out there with economic models more bonkers than this?! | 41 comments on LinkedIn

Advancing medical knowledge from research to practice

L'engagement est le vrai angle mort des démarches de veille : on investit dans l'outil, les sources, le paramétrage… et on oublie la question la plus décisive : comment faire vivre la démarche dans le temps ?

Résultat : beaucoup de lecteurs passifs, quelques contributeurs, et une dynamique qui repose sur deux ou trois personnes. Ce n'est pas un problème d'outil mais un enjeu d'engagement et ça, ça se travaille !

RDV mercredi 7 octobre 2026, pour un webinaire de 45 minutes, à l'occasion de la sortie de notre livre blanc « Engager au sein des démarches de veille ». Pas de recettes magiques mais des grilles de lecture issues du terrain et des sciences sociales, et des retours d'expérience de démarches qu'on a accompagnées 😉

Inscription (gratuite) : https://lnkd.in/eqRFHJVv

#Veille #IntelligenceÉconomique #Engagement #VeilleCollaborative

"The Death of the Academic Author? Implications of Artificial Intelligence for Academic Writing and Publishing."

That was the title of a symposium I had the chance to attend at the AMEE - The International Association for Health Professions Education conference in August. Jennifer Cleland moderated, and the panel was Lorelei Lingard, Erik Driessen, Yu-Che Chang, Ayelet Kuper, and Ken Masters.

A title like that plus this line-up of speakers? Sign me up!

You would anticipate excellent debate, and it lived up to the billing. I had a lot of thoughts immediately after the session, but I finally had a chance to sit down and write/think/dictate my reflections.

Wait! Write/think/dictate?!? What does that mean? You'll just have to read it to find out! 😉
The link is in the first comment.

Then let me know your thoughts on writing as thinking versus writing as writing in the comments. | 14 comments on LinkedIn

Les premiers résultats de l'enquête par questionnaire pilotée par le Gis Marsouin sur les usages de l'IAGen par les étudiants, obtenus sur un échantillon de 2 276 étudiants, viennent nourrir nos réflexions en matière de recherche mais également d'enseignement.

- Un usage largement répondu
85% des étudiant·es interrogé·es l’utilisent dans le cadre de leurs études.

- Pour réviser et réaliser des "devoirs maison"
75% l’utilisent pour réviser des examens, plus de 60% pour préparer un TD ou un TP ou réviser un cours magistral, et plus d’un tiers lors d’un examen réalisé à domicile.

- Peu discuté avec les enseignants
Malgré un fort taux d'usage, plus de la moitié des étudiant·es déclarent que l’usage de l’IAG n’a pas été discuté avec leurs enseignant·es et seul·es 18% rapportent une autorisation explicite.

- Confiance et fiabilité limitées mais peu de vérification
D'un côté, les étudiant·es interrogé·es portent un regard globalement prudent sur les productions de l’IAG : seul·es 16% les jugent fiables et 11% considèrent les informations fournies comme exactes. De l'autre, cette faible confiance ne se traduit pas systématiquement par un contrôle des réponses : 36% vérifient systématiquement les sources.

- Un compagnon virtuel ?
En dehors des usages académique, l'enquête explore également une pratique spécifique : le recours à l’IAG comme « compagnon virtuel », pour discuter, obtenir des conseils ou rechercher un soutien émotionnel. Encore minoritaire, elle concerne tout de même 17% des étudiant·es et est davantage associée à la solitude et à l’anxiété déclarées.

Le questionnaire a été construit par Agnès Grimault-Leprince, Bruno Daucé, Marianne Lumeau, Laurent Mell et notre regrettée collègue Séverine Erhel.
Les données ont été traitées par @Pierre Le Bras, qui a rédigé la note complète à retrouver sur le site de Marsouin : https://lnkd.in/e6hiPR7S

Reading 100 research papers is not the same as understanding 100 research papers.
The real challenge in a literature review is turning a large collection of papers into clear themes, relationships, contradictions and research gaps.

This framework gives you 20 practical prompts to help analyse 100 papers and move from scattered information to structured research themes.

You can use these prompts to:
• Extract key information from papers
• Identify major topics and recurring themes
• Compare findings and research methods
• Find contradictions and research gaps
• Map relationships between themes
• Identify emerging trends
• Build a thematic table
• Develop a literature review structure
• Connect themes to your research topic
• Refine and validate the final themes

AI can help you process and organise information, but the interpretation and final research judgment should remain yours.

#ResearchMethodology #AcademicResearch #ResearchTools #ResearchGap #SystematicReview #Researchers | 10 comments on LinkedIn

As of October 1, arXiv is updating our submission rate limit policy across all submitters and categories.

arXiv, and the scientific community at large, are facing a watershed moment. Scholarly publishing is currently changing at a rapid pace, and we are seeing a massive transformation in how researchers communicate their results. With AI tools becoming more advanced and accessible to authors, the "practical limit" of how many papers can be submitted is now up for debate.

Open access repositories across the board are seeing a sharp increase in the number of submissions per author, per month. In September of 2016, arXiv received 9,869 submissions. In September of 2024, arXiv received 20,569 submissions. This September, arXiv received 40,363 submissions, which in turn generated almost 9,000 support tickets for arXiv staff and moderators. In only the past two years, submissions have doubled.

To support fair moderation and equitable access in the age of AI, arXiv now permits two submissions per calendar month for all submitters. This updated policy is being implemented across all submitters and categories as a stopgap while we determine what may be the new best practice for authors employing more and more advanced AI tools, and to give us time to improve our moderation tools and procedures accordingly.

Read our full announcement here: https://lnkd.in/euW4nCmf
| 81 comments on LinkedIn

One default AI tab can quietly become a daily bottleneck.

𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲 𝗖𝗵𝗶𝗲𝗳 𝗔𝗜 𝗟𝗲𝗮𝗱𝗲𝗿.
𝗘𝗮𝗿𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀.
𝗚𝗲𝘁 𝗔𝗜 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗻𝗼𝘄 → aiforleaders.com/cp
__________

Give each tool a clear job before your team adds more tabs.
Use this five-part stack as a starting point to test.
The roles overlap. No tool owns a whole category.

1 → ChatGPT for quick ideas
Draft email rewrites, explore angles and get coding help.
Ask for three options with one clear difference each.
Pick a direction, then check the details before use.

2 → Claude for deep work
Bring a complex document, report or deck outline.
State the decision and the audience before asking for a draft.
Ask it to flag gaps and separate facts from assumptions.
Keep a human owner for the final recommendation.

3 → Perplexity for current research
Start with a question that needs recent sources.
Ask for dates, evidence and links you can open.
Check whether each citation supports the actual claim.
A cited answer still needs a source check.

4 → Gemini + Nano Banana for visuals and Google work
Use Gemini for document work and Workspace context.
Nano Banana is image generation within Gemini.
Give it the subject, layout and words a visual must contain.
Check image text and file limits before relying on output.
Access and integrations depend on your plan and setup.

5 → Copilot for Microsoft work
Try it where the task already lives: Excel, Word or PowerPoint.
Use Outlook help for a draft tied to the thread.
Check formulas against known values and read before sending.
Confirm your Microsoft 365 plan supports the feature.

Use five minutes as a switching cue, not a proven cutoff.
If the answer keeps failing, first check the brief.
Missing context will follow you into the next tool.
Then try another tool on the same task and compare results.

Make every brief include goal + audience + format.
For example: turn these notes into a CEO update.
Use six short points and flag missing decisions.
Define a useful result before you judge the draft.

For repeat work, save context in a project or agent.
Start with meeting notes, replies or a weekly report.
Keep one good example and a short review checklist.
Test on real work before giving an agent broader actions.

Track time spent checking and fixing the output.
Keep the tools that reduce that total for your team.
More subscriptions alone will not improve the workflow.
Choose one recurring task to test this week.

Which job are you still forcing your default AI tool to do? | 29 comments on LinkedIn

🔬 AI Tools for Research & Review Article Writing

AI can significantly accelerate research, but the researcher must remain responsible for scientific judgment, originality, accuracy, and integrity.

A practical workflow is

🔹 Research Question & Planning: ChatGPT, Claude, Gemini, Perplexity
🔹 Literature Discovery: Scopus, Web of Science, Google Scholar, Semantic Scholar, OpenAlex
🔹 Related Paper Discovery: ResearchRabbit, Connected Papers, Litmaps, Lens
🔹 Literature Screening & Synthesis: Elicit, Consensus, Scite, SciSpace
🔹 Reference Management: Zotero, Mendeley, EndNote
🔹 Bibliometric Analysis: VOSviewer, Bibliometrix/Biblioshiny, R, Python
🔹 Article Drafting & Refinement: ChatGPT, Claude, Gemini, Copilot
🔹 Academic Language Editing: Grammarly, Paperpal, Writefull, LanguageTool
🔹 Figures & Visualization: BioRender, Canva, PowerPoint, Python, R
🔹 Citation Verification: Crossref, PubMed, Google Scholar, Scite
🔹 Originality & Quality Checks: Turnitin, iThenticate, journal guidelines
🔹 Final Submission: Journal submission system and AI disclosure requirements

⚠️ Ethical AI Principles

✅ Use AI as an assistant, not a substitute for the researcher.
✅ Verify every reference, DOI, claim, statistic, and interpretation.
❌ Never fabricate references, data, or results.
❌ Never submit AI-generated text without critical human review
✅ Protect confidential and unpublished research data.
✅ Follow the target journal's AI use policy.
AI can accelerate the research process.
Scientific contribution, critical thinking, and responsibility remain with the researcher.

Dr. Raffi Mohammed
Professor | Department of Mechanical Engineering
Ramachandra College of Engineering, Eluru, Andhra Pradesh, India


#ArtificialIntelligence #AIforResearch #ResearchTools #ReviewArticle #ResearchMethodology #AcademicWriting #BibliometricAnalysis #VOSviewer #Bibliometrix #SystematicReview #ResearchIntegrity #AcademicResearch #ScholarlyPublishing #ResponsibleAI #Researcher #MechanicalEngineering

What is the most appropriate use of generative AI (e.g., ChatGPT) in research?
This was the question I heard most often this summer when interacting with junior scholars (PhD students and assistant professors).
My initial instinct was to say NONE. Zero use of AI in management research.
After all, many of us received our doctoral training when AI existed mainly in science fiction. Remember Star Trek?
We learned to write, rewrite, and revise every sentence, agonize over every word and punctuation mark, and second-guess every phrase.
For those of us who learned English as a second, third, or fourth language, meeting the writing standards of leading journals was especially challenging. But we persisted and learned.
When I advocated zero tolerance for generative AI in academic writing and reviewing, however, my views did not seem to resonate, particularly with younger scholars. For them, AI feels far less unfamiliar than it does to some of us from an earlier generation.
This fall, the The University of Alabama offered UA AI Experience, a three-hour, self-paced online course on responsible generative AI use. I took it and came away with a clearer perspective.
My key takeaway: Use generative AI as an editor, not as a substitute for your own thinking and writing.
Consider this post. I developed the idea, drafted the text in Microsoft Word, and worked to ensure it reflected what I wanted to say, a process that is not always easy when you do not think in English.
Only after I was satisfied with the substance did I ask ChatGPT to polish the writing.
That is fundamentally different from asking ChatGPT to write the post from a few prompts.
At least, that is how I interpreted the lessons from UA AI Experience.
Why does this distinction matter?
Using AI to edit and refine writing can help level the playing field. Many scholars, including senior academics and native English speakers, have relied on professional copyeditors to sharpen their prose.
But these services can be expensive and inaccessible to scholars outside well-resourced institutions.
Generative AI puts a capable writing editor within reach of almost everyone, often at little or no cost.
Asking AI to generate the substance of scholarly writing raises different questions about intellectual ownership, integrity, and authorship.
AI cannot take responsibility for research as a human author can, and many journals restrict or prohibit listing AI tools as authors.
Thanks to UA AI Experience, I now have a more thoughtful answer when asked about appropriate AI use in research.
But I may still be wrong (as I often am).
Where do you draw the line between using AI to improve your writing and using it to do your writing?
I welcome your perspectives in the comments or via direct message!
Jef Naidoo Pratyush Nidhi Sharma Kay Palan Dr.Jaspreet Kaur Banu Goktan Bilhan Sandra Mortal Robert Hayes Peter Mohler Athina Skiadopoulou, Ph.D. Jeffrey Martin Craig Armstrong

“I will miss the creativity of teaching.”

On #WorldTeachersDay, check out this Working Life from a retired professor emeritus on how she challenged students to think beyond facts—and how she learned to teach like a scientist. https://scim.ag/4e3ygGr