Monthly Archives: July 2026

Beyond ONOS and APCs: a framework for Diamond Open Access publishing in India

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I have spent a working life moving between anthropology, library science, and the National Informatics Centre. An odd trio, I’ll admit. But each one taught me a version of the same truth: own the pipes, and you decide what flows through them. Coming to India’s scholarly communication strategy, it has been shaped for decades by two models, and both of them leave the pipes in someone else’s hands.

The first is the subscription model, now consolidated at national scale through One Nation One Subscription (ONOS). The second is the Article Processing Charge, or APC, model, where authors or their institutions pay publishers to make a single article openly accessible. Each solves a real problem. ONOS gives Indian researchers access to the world’s literature. APCs let Indian researchers publish openly instead of hiding their work behind a paywall.

But look closely and both models share the same weakness. They send public money outward, year after year, into systems India does not own and cannot govern.

Where the current approach runs out of road

ONOS helps a researcher read. It does nothing to build Indian publishing capacity. When the subscription cycle ends, India is exactly where it started: dependent on external publishers for access to knowledge, including knowledge India itself produced.

APCs have the opposite problem. They help a researcher publish, but they do it one paper at a time, transferring public research money to a publisher with every article. As India’s publication output grows, so does the bill. The model rewards volume, not sustainability, and India, being a very large producer of research, ends up paying for its own success.

Put the two together and you get a continuous outflow of public funds, with almost nothing to show for it once the money has left the country. This is the paradox I keep returning to: India is one of the largest producers of research in the world, and yet the infrastructure that carries that research to its readers belongs to someone else.

There is a third path. I want to make the case for it here.

Diamond Open Access

Picture a scholarly ecosystem where authors publish without paying an APC, readers read without a subscription, and the journals themselves are owned by universities, scholarly societies, and public institutions rather than commercial publishers. Publishing is treated as public infrastructure, funded the way we fund roads or electricity, not billed to authors and readers as if it were a private service.

That is Diamond Open Access: neither author nor reader pays, and the cost of running the system is carried by institutions, government, and scholarly bodies acting together. The shift in thinking is small but consequential. Instead of paying for articles one at a time, India would be investing in the plumbing.

A national framework, pillar by pillar

1. A National Diamond Publishing Mission. Someone has to hold this together, and I don’t think it can be one ministry acting alone. The Ministry of Education, the Department of Science and Technology, UGC, AICTE, ICSSR, ICMR, and INFLIBNET would need to coordinate policy, standards, and funding under a single mission.

2. A National Scholarly Publishing Platform. Every university trying to run its own journal hosting, peer review, and DOI infrastructure is reinventing a wheel it does not need to build. A shared, publicly funded platform, offering journal hosting, editorial workflow, peer review support, DOI assignment, ORCID integration, metadata services, long-term preservation, and usage analytics, would let institutions focus on the scholarship instead of the plumbing.

3. Strengthening Indian journals. Not more journals, better ones. A national programme could identify quality Indian journals and back them with technical infrastructure, editorial training, indexing support, and publishing standards, so they compete on merit rather than merely existing.

4. Reviving the university press. Look at the great universities elsewhere and you find a press attached to almost every one of them. India’s universities largely lack this. Digital university presses, open access monographs, textbooks, conference proceedings: every major Indian university should have the means to publish its own intellectual output rather than sending it elsewhere to be published back to us.

5. A national repository network. Shodhganga, institutional repositories, datasets, preprints, government-funded research: all of it should sit in an interoperable network so that publicly funded knowledge stays permanently and freely accessible to the public that paid for it.

6. AI-assisted publishing infrastructure. This is the part closest to my own current work, and I think it is underrated. Used well, AI can bring down the cost of publishing considerably, handling language polishing, metadata generation, reviewer matching, accessibility checks, reference validation, and translation into Indian languages, while editorial judgement stays firmly with human editors.

7. Reforming research assessment. None of this works if institutions keep counting publications and chasing impact factors. Assessment needs to move toward research quality, reproducibility, data sharing, and societal impact, so that researchers are rewarded for what they contributed, not for which journal’s masthead carried their name.

Can India afford it

I would put the question the other way round. The real question is not whether India can afford to build this infrastructure. It is whether India can afford another decade of not building it.

Even a modest redirection of the money currently spent on subscriptions and APC reimbursements could fund national publishing platforms, repository services, journal modernisation, editorial capacity building, and AI-enabled publishing tools. Subscription fees and APC payments buy access for a year and then vanish. This kind of investment buys an asset that stays inside the research ecosystem.

The choice in front of us

India’s digital transformation already taught this lesson once. Aadhaar, UPI, DigiLocker: these succeeded because India chose to build shared public infrastructure instead of renting someone else’s. Scholarly communication deserves the same ambition.

The choice is not between access and openness. It is between paying, indefinitely, for access to systems owned by others, and building a publishing ecosystem the academic community owns for itself.

ONOS may improve access today. APC funding may enable publication today. Diamond Open Access offers something that outlasts today: a scholarly communication system that is equitable, sustainable, nationally owned, and built for the public purpose research is supposed to serve in the first place.

For a country that wants to be a global knowledge leader, that may be the one investment that matters most.

From AI adoption to AI governance: libraries are growing up

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Somebody in last week’s AI4LIB Open Hour asked a question that stopped the room for a second. Not “should we use ChatGPT in the library,” which used to be the question every single week for the better part of a year. This time it was, “who in my library is actually responsible if the AI gets something wrong?” Nobody on the call had a ready answer. That silence, more than any survey or dataset, told me the profession has quietly crossed over from one debate into another.
For most of the past two years, the AI conversation in libraries was about adoption. Should we try it. Which tool. Is it allowed. That question is more or less settled now. AI already sits inside cataloguing, reference, discovery, and research support, whether a library formally approved it or not. The question worth asking today is not whether AI belongs in the library. It is how a library governs something that is already inside its walls.
Start with the job market, because numbers rarely lie the way opinions do. Going through more than 800 library and information science job advertisements across India, the pattern is hard to miss. MLIS, UGC-NET, SET, a Ph.D. where the post demands it, these still open the door. But almost every listing now expects working familiarity with Koha or DSpace, comfort with MARC21 and Dublin Core, exposure to research databases and plagiarism tools, and increasingly, some demonstrated ability to use AI responsibly. I won’t be wrong if I say the degree gets you shortlisted, the skillset gets you hired. For a student writing to me on WhatsApp asking what to learn next, that is the honest answer, not the diplomatic one.
Governance is where things get interesting, and where most libraries are furthest behind. Walk into any library today and you will find staff already using ChatGPT, Gemini, Copilot, NotebookLM, on their own initiative, often on their own devices, usually without telling anyone. Very few institutions have written down what is and is not acceptable. The University of Arizona Library’s AI strategy is worth a close read here, precisely because it does not obsess over which tool to bless. It builds around accountability, who decides, staff training, privacy, transparency, and risk. That is the correct instinct. We went through the same lesson with the internet, then with institutional repositories, then with open access. The tool is never the hard part. The framework around the tool is.
This is where I keep returning to a phrase I have used before on this page: the Fluency Trap. AI can produce an answer that reads beautifully, cites confidently, and is completely wrong. Fluency and accuracy are not the same thing, and a profession built on evaluating information for a living cannot afford to forget that distinction just because the output sounds polished. If anything, AI-generated fluency raises the stakes on the oldest skill in librarianship: teaching people to ask, how do you know this is true.
Naturally the question that keeps coming back, in every workshop and every Open Hour, is whether AI will replace librarians. My answer has not changed and I don’t expect it to. AI has already absorbed the routine end of the job, quick factual lookups, first-pass metadata, basic retrieval. It has not touched, and I doubt it will soon touch, the part of librarianship that was never really about retrieval in the first place. Helping a confused research scholar turn a vague worry into a searchable question. Knowing which gap in the literature actually matters. Reading a community and knowing what it needs before it asks. That is judgement, and judgement does not come out of a training corpus.
There is a quieter opportunity hiding inside all this anxiety, and it belongs to public libraries as much as academic ones. Communities need someone to teach them how AI works, how to spot AI-generated misinformation, how to make an informed choice instead of an easy one. Public libraries are already stepping into that role in a few places, and it fits the mission better than almost anything else on offer right now. Academic and research libraries have their own version of the same opening, in metadata work, in supporting open science, in shaping how AI gets used across a campus rather than reacting to it department by department.
None of this sits in settled law, and it will not for some time. Copyright questions around AI training data, authorship, and licensing are moving through courts across the world as I write this, with well over 150 cases already filed. For anyone using AI in research or teaching, that uncertainty is not a footnote, it is a reason to be deliberate about disclosing what the AI did and what the human did.
If this week’s discussions distilled into one line, it would be the same three instructions I keep repeating on this Open Hour, and I make no apology for repeating them again: verify the source, disclose the tool, validate the output. Simple enough to fit on a sticky note. Hard enough that most institutions still haven’t managed it. Libraries did not ask to become the profession that teaches the world how to think about AI responsibly. But look around, nobody else is doing it half as well, and somebody has to.

What recent trends in AI for libraries actually tell us

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I was scrolling through library news early this week, the way I do most mornings with my tea, and something struck me. Six or seven stories, from six or seven different corners of the world, all circling the same question without quite naming it: who is in charge of AI in a library, and what happens if nobody decides on purpose?

Let me walk you through what I found, because I think it matters more to us in India than the datelines suggest.

Start in Chicago. At the ALA Annual Conference 2026, a panel sat down to talk about the ethical use of AI in libraries, and the conversation went exactly where I expected it to go: privacy, transparency, intellectual freedom, equitable access. Nothing new there, you might say. Librarians have guarded these values for a century. But here is the twist. The panel’s real message wasn’t a list of principles, it was a single instruction: don’t adopt AI simply because it exists on a shelf. Ask what it does to your users first. I have said something close to this for two years now to anyone who will listen, so forgive me if I nodded a little too hard at my screen.

Meanwhile, Fairfield University quietly updated its AI Literacy Guide, and this one deserves more attention than it will get. The guide tells students to use AI for generating ideas, sharpening research questions, exploring topics they don’t yet understand. Fair enough. But it also insists, in plain language, that AI-generated content should never be accepted without review. That single line is, in my view, the whole future of reference service compressed into one sentence. We spent decades teaching students to evaluate a source. Now we are teaching them to evaluate an answer that has no source at all until you go looking for one. I have called this the Fluency Trap in my own writing: an AI response reads so smoothly that we mistake the writing for the truth. Fairfield just built that lesson into policy.

Behind all this front-of-house discussion, something quieter is happening in technical services, and it happens to be a subject close to my heart. More than 860 cataloguers across 77 countries are now using AI-assisted tools to process MARC21 records from photographs and PDFs. I have run this experiment myself, at a much smaller scale, with a Llama model on my own machine, cataloguing books photographed on a mobile phone. The accuracy was good, not perfect, somewhere around three-quarters at the field level in my own testing. What struck me both times, once in my study and once reading this week’s numbers, is that the tool doesn’t replace the cataloguer’s judgement. It just clears the underbrush so the judgement has somewhere to stand.

And this is where a new figure starts appearing in library conversations: the AI Librarian. Not a science fiction character, just a colleague who understands information science and AI well enough to sit between the two. Designing literacy programmes, checking tools for bias, supporting academic integrity, writing institutional policy. Whether the job title ever appears on an appointment letter in India is beside the point. The skills already do. I would go so far as to say I have been doing a version of this job since I retired from NIC, minus the letterhead.

The University of North Carolina Libraries updated its guidance on AI for evidence synthesis, running something called a Library AI Studio with workshops and consultations. The Conference of European National Librarians is collecting input from national libraries on text datasets and AI models for a forthcoming white paper. And the San José Public Library keeps expanding its AI Center for Civic and Social Good, teaching ordinary members of the public what these tools actually do under the bonnet. Different countries, different budgets, same instinct: don’t just use AI, teach people to see through it.

So where does India stand in all this? Closer than we usually give ourselves credit for. I see workshops on AI cataloguing and discovery systems happening at library schools across the country. I see young colleagues experimenting with digital repositories linked to platforms like e-Granthalaya. What I don’t yet see enough of is the governance layer, the part where an institution sits down and decides, on paper, what AI is and isn’t allowed to touch in its library. Ethical frameworks, staff training, human oversight, community access. These aren’t Chicago’s problems or Fairfield’s problems. They are library problems, and they arrived here the same week they arrived there, whether we noticed or not.

I keep coming back to a small line I wrote for myself a while ago, my Librarian’s 3-Step Verification Protocol: verify the source, disclose the tool, validate the output. It sounds almost too simple to matter. But then again, so did “wash your hands,” until it saved a great many lives. Some rules earn their keep precisely by being obvious enough to actually follow.

The question this week’s stories leave me with isn’t whether AI will change libraries. That ship, as they say, has long sailed. It’s whether we will be the ones steering it, or whether we’ll simply be told, one policy document at a time, where it has already gone.

References

Library Journal, “Ethical Use of AI in Libraries,” ALA Annual 2026 coverage: https://www.libraryjournal.com/story/ethical-use-of-ai-in-libraries-ala-annual-2026

Fairfield University, DiMenna-Nyselius Library, Artificial Intelligence Literacy Guide: https://librarybestbets.fairfield.edu/c.php?g=1480414&p=11031941

Scholaro, “The AI Librarian”: https://www.scholaro.com/db/News/ai-librarian-college-383

UNC Libraries, AI and Machine Learning guidance: https://guides.lib.unc.edu/AI-ML

CENL (Conference of European National Librarians), AI in Libraries Network Group: https://www.cenl.org/text-datasets-and-ai-models-input-needed-from-european-national-libraries/

San José Public Library, AI Center for Civic and Social Good: https://www.sjpl.org/artificial-intelligence/

What 800+ Library Job Notices Taught Me About the Future of Library Science

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For the past year, I have been quietly following the ijLSim WhatsApp group, run tirelessly by Dr. U. Pramanathan. Almost every day, without fail, he shares library-related opportunities from across India—walk-in interviews, contractual appointments, deputation vacancies, assistant librarian posts, workshops, webinars, and much more.

Between July 2025 and July 2026, the group accumulated 825 messages. As I worked through them, I found myself asking a simple question:

What are Indian institutions really looking for in Library and Information Science (LIS) professionals today?

The first answer was reassuringly familiar.

An MLIS degree still remains the primary gateway into the profession. UGC-NET or SET continues to be one of the most common eligibility requirements, appearing in roughly seven out of every ten recruitment notices I reviewed. A Ph.D., meanwhile, is becoming increasingly important for senior positions such as Librarian and Deputy Librarian, moving from a preferred qualification to an explicit requirement in many cases.

Anyone who graduated in 2005, 2010, or even a few years later would recognise this part of the landscape immediately.

But there was a second layer to the story.

And that is where things became interesting.

Koha appeared 37 times across the postings. Not “library automation” as a broad concept, but Koha itself—named directly and repeatedly. DSpace followed with 11 mentions. INFLIBNET, N-LIST, Shodhganga, DELNET, and Scopus also appeared frequently, not as passing references but as expected areas of working knowledge.

This suggests a noticeable shift. Knowing cataloguing rules, MARC fields, or classification schemes alone is no longer enough. Employers increasingly expect candidates to have practical experience. They want professionals who have worked with Koha’s staff interface, configured an OPAC, managed circulation modules, uploaded theses into DSpace repositories, and handled digital library workflows in real-world settings.

Then another trend appeared.

Quietly, but unmistakably.

Artificial Intelligence entered the conversation.

Across the notices, I found 18 references to AI or Artificial Intelligence, with nine specifically mentioning “AI tools” as a desirable skill. These were not casual buzzwords added for effect. They appeared alongside communication skills, computer literacy, and other core competencies.

That caught my attention.

I have written previously about what I call the Trust Paradox—the idea that the more confidently an AI system presents information, the more likely people are to accept it without verification. Looking through these job notices, I realised employers are arriving at a similar conclusion.

They are not simply looking for people who can use AI.

They are looking for people who understand where AI helps, where it fails, and when human judgement is still essential.

In other words, fluency is not competence.

And increasingly, institutions expect candidates to demonstrate both.

So, if a young LIS graduate were to ask me over a cup of tea what they should focus on today, my answer would be straightforward:

Your degree gets you into the interview room.

Your NET qualification helps you clear the first shortlist.

But practical skills—Koha on your laptop, DSpace on your résumé, and a clear understanding of what AI tools can and cannot do—are what make you stand out in a competitive field.

Based on this review, I developed the following checklist. It is organised not as a list to read once and forget, but as a roadmap students can actively work through.

LIS Job Readiness Checklist

Based on an analysis of 200+ recruitment notices shared in the ijLSim WhatsApp group between July 2025 and July 2026.

Educational Qualifications

  • MLIS / M.Lib.I.Sc. – Minimum qualification for most Librarian and Assistant Librarian positions
  • BLIS – Commonly required for Library Assistant and junior professional roles
  • UGC-NET or NET/SET in Library and Information Science
  • Ph.D. – Increasingly expected for senior academic library positions
  • M.Phil. – Less common today, but still preferred by some institutions

Core Library Automation Skills

  • Koha – The most frequently mentioned library software across the postings
  • DSpace – Repository management and institutional archiving
  • MARC21 cataloguing standards
  • DDC and UDC classification systems
  • Metadata standards such as Dublin Core
  • RFID and library automation hardware basics
  • OPAC configuration and troubleshooting

Digital Resource and Research Support Skills

  • INFLIBNET and N-LIST administration
  • Shodhganga submission workflows
  • DELNET resource sharing and networking
  • Scopus and Web of Science navigation
  • Bibliometric indicators, citation tracking, and impact factor awareness
  • Plagiarism detection tools such as Turnitin and similar platforms

Emerging Skills That Differentiate Candidates

  • Practical AI tool literacy for reference services, literature searches, and workflow support
  • Understanding AI limitations, including hallucinated references and verification requirements
  • Basic data analytics for reporting and assessment
  • Data visualisation tools such as Canva and Google Looker Studio
  • Familiarity with open-source library technologies

Computer Literacy

  • MS Word, Excel, and PowerPoint
  • Basic website and content management skills
  • Digitisation workflows and digital document handling

Soft Skills

  • Strong communication skills, particularly English proficiency
  • User support and interpersonal skills
  • Leadership and team management capabilities for senior roles
  • Attention to detail for cataloguing and records management
  • Ability to work independently and take initiative

The Bottom Line

The traditional pathway into Indian librarianship remains largely unchanged. An MLIS degree combined with NET qualification continues to open most doors.

What is changing is the layer above that foundation.

Today, the strongest candidates combine formal qualifications with practical technology skills. They understand Koha, can work with digital repositories, are comfortable using AI tools responsibly, and possess a basic understanding of data and analytics.