Impact Factor of Journals: What You Need To Know
Mar 11, 2025 |
12 Mins ReadThe impact factor of journals is a crucial academic publishing metric, serving as a measure of a journal's influence and importance within its field. For you, as a researcher, and institutions alike, understanding this journal classification is essential for making informed decisions about where to publish and which journals to follow.
What is Impact Factor?
The Impact Factor was created by Eugene Garfield, the founder of the Institute for Scientific Information (ISI). Garfield first mentioned the idea of an impact factor in Science magazine in 1955.
In the early 1960s, Eugene Garfield with the help of Irving H. Sher created the Journal Impact Factor (JIF) to help select journals for the Science Citation Index (SCI). They developed this metric by re-sorting the researcher citation index into a journal citation index.
Initially, the Impact Factor was used internally by ISI to compile the Science Citation Index. In 1975, ISI began publishing the Journal Citation Reports (JCR), which included the Impact Factor calculations for journals.
How is the Impact Factor of Journals Calculated?
By calculating the average number of citations received by articles published in those journals over a set period of time, typically two years.
For example, the 2022 impact factor of journals is calculated as follows:
Journal Impact Factor (JIF) = A / B
Where:
- A = Total number of citations in a given year (e.g., 2023) to articles published in the previous two years (e.g., 2021 and 2022).
- B = Total number of citable items (articles, reviews, etc.) published in those same two years (2021 and 2022).
What Does the Impact Factor of Journals Tell a Researcher?
The impact factor of journals provides you with valuable insights into a journal's influence and importance within its field. Here's what the impact factor tells you:
1. Journal Quality: A higher impact factor generally indicates a more prestigious and influential journal in its discipline. This can help you identify high-quality publications for your work.
2. Citation Frequency: The impact factor reflects the average number of citations received by articles published in the journal over a specific period. This indicates how frequently the journal's content is cited by other researchers.
3. Visibility and Reach: Journals with higher impact factors tend to have broader readership and greater visibility in the academic community. Publishing in these journals can increase the exposure of your research.
4. Research Influence: The impact factor of journals can serve as a proxy for the potential influence of research published in a particular journal. It suggests how impactful the average article in that journal might be.
5. Career Advancement: Publishing in high impact factor journals can be crucial for academic and professional advancement, often considered in tenure decisions, grant applications, and professional evaluations.
6. Comparative Tool: Researchers can use the impact factor to compare journals within the same field, helping them make informed decisions about where to submit their work.
However, it's important to note that the impact factor has limitations. It doesn't measure the quality of individual articles, and it can be influenced by factors such as the number of review articles a journal publishes. You should consider the impact factor alongside other metrics (e.g., SJR scores), and qualitative assessments when evaluating journals for your research.
What is a good impact factor?
The impact factor (IF) is a metric used to evaluate the influence and quality of academic journals by measuring the frequency with which their articles are cited. Generally, a higher impact factor indicates a more influential journal within its field. However, "good" impact factors vary significantly across different disciplines. For instance, in biochemistry, impact factors are often categorized as follows:
- Good: 2–4
- Great: 5–8
- Awesome: 9–14
- Excellent: Above 14
It's important to note that these ranges are approximate and can vary based on specific research areas. Additionally, while impact factors provide insight into a journal's citation frequency, they do not necessarily reflect the methodological quality or societal impact of individual articles. Therefore, when assessing research quality, it's advisable to consider multiple metrics alongside the impact factor.
What are the Highest Impact Factor Journals
Some of the top impact factor journals include:
- Medical and Life Sciences
- CA-A Cancer Journal for Clinicians (254.7)
- The New England Journal of Medicine (91.245)
- The Lancet (79.321)
- Nature Reviews Molecular Cell Biology (94.444)
- Multidisciplinary Sciences
- Nature (50.5)
- Science (47.728)
- Physical Sciences
- Chemical Reviews (60.622)
- Nature Materials (43.841)
- Nature Nanotechnology (39.213)
- Environmental Sciences
- Energy & Environmental Science (38.532)
- Nature Geoscience (16.908) - Computer Science and Engineering
- IEEE Transactions: Systems, Man, and Cybernetics (13.451)
These top-tier journals represent the pinnacle of academic publishing, often featuring groundbreaking research and influential studies.
Academic Journal Impact: Beyond the Numbers
While the impact factor of journals is a valuable journal ranking, it's important to consider other factors when evaluating academic influence:
- Field-specific considerations: Impact factors can vary significantly between different academic disciplines
- Citation patterns: Some fields have faster citation cycles than others, affecting impact factor calculations.
- Journal scope: Specialised journals may have lower impact factors but still be highly influential in their niche.
Conclusion
Understanding the impact factor of journals is crucial for researchers navigating the academic publishing landscape. While it's a valuable metric, it should be considered alongside other factors when evaluating journal quality and influence. By staying informed about impact factors and their implications, researchers can make more strategic decisions about where to publish their work and maximise the visibility and impact of their research.
Why AI like ChatGPT still quotes retracted papers?
AI models like ChatGPT are trained on massive datasets collected at specific moments in time, which means they lack awareness of papers retracted after their training cutoff. When a scientific paper gets retracted, whether due to errors, fraud, or ethical violations, most AI systems continue referencing it as if nothing happened. This creates a troubling scenario where researchers using AI assistants might unknowingly build their work on discredited foundations. In other words: retracted papers are the academic world's way of saying "we got this wrong, please disregard." Yet the AI tools designed to help us navigate research faster often can't tell the difference between solid science and work that's been officially debunked. ChatGPT and other assistants tested Recent studies examined how popular AI research tools handle retracted papers, and the results were concerning. Researchers tested ChatGPT, Google's Gemini, and similar language models by asking them about known retracted papers. In many cases, they not only failed to flag the retractions but actively praised the withdrawn studies. One investigation found that ChatGPT referenced retracted cancer imaging research without any warning to users, presenting the flawed findings as credible. The problem extends beyond chatbots to AI-powered literature review tools that researchers increasingly rely on for efficiency. Common failure scenarios The risks show up across different domains, each with its own consequences: Medical guidance: Healthcare professionals consulting AI for clinical information might receive recommendations based on studies withdrawn for data fabrication or patient safety concerns Literature reviews: Academic researchers face citation issues when AI assistants suggest retracted papers, damaging credibility and delaying peer review Policy decisions: Institutional leaders making evidence-based choices might rely on AI-summarised research without realising the underlying studies have been retracted A doctor asking about treatment protocols could unknowingly follow advice rooted in discredited research. Meanwhile, detecting retracted citations manually across hundreds of references proves nearly impossible for most researchers. How Often Retractions Slip Into AI Training Data The scale of retracted papers entering AI systems is larger than most people realise. Crossref, the scholarly metadata registry that tracks digital object identifiers (DOIs) for academic publications, reports thousands of retraction notices annually. Yet many AI models were trained on datasets harvested years ago, capturing papers before retraction notices appeared. Here's where timing becomes critical. A paper published in 2020 and included in an AI training dataset that same year might get retracted in 2023. If the model hasn't been retrained with updated data, it remains oblivious to the retraction. Some popular language models go years between major training updates, meaning their knowledge of the research landscape grows increasingly outdated. Lag between retraction and model update Training Large Language Models requires enormous computational resources and time, which explains why most AI companies don't continuously update their systems. Even when retraining occurs, the process of identifying and removing retracted papers from massive datasets presents technical challenges that many organisations haven't prioritised solving. The result is a growing gap between the current state of scientific knowledge and what AI assistants "know." You might think AI systems could simply check retraction databases in real-time before responding, but most don't. Instead, they generate responses based solely on their static training data, unaware that some information has been invalidated. Risks of Citing Retracted Papers in Practice The consequences of AI-recommended retracted papers extend beyond embarrassment. When flawed research influences decisions, the ripple effects can be substantial and long-lasting. Clinical decision errors Healthcare providers increasingly turn to AI tools for quick access to medical literature, especially when facing unfamiliar conditions or emerging treatments. If an AI assistant recommends a retracted study on drug efficacy or surgical techniques, clinicians might implement approaches that have been proven harmful or ineffective. The 2020 hydroxychloroquine controversy illustrated how quickly questionable research spreads. Imagine that dynamic accelerated by AI systems that can't distinguish between valid and retracted papers. Policy and funding implications Government agencies and research institutions often use AI tools to synthesise large bodies of literature when making funding decisions or setting research priorities. Basing these high-stakes choices on retracted work wastes resources and potentially misdirects entire fields of inquiry. A withdrawn climate study or economic analysis could influence policy for years before anyone discovers the AI-assisted review included discredited research. Academic reputation damage For individual researchers, citing retracted papers carries professional consequences. Journals may reject manuscripts, tenure committees question research rigour, and collaborators lose confidence. While honest mistakes happen, the frequency of such errors increases when researchers rely on AI tools that lack retraction awareness, and the responsibility still falls on the researcher, not the AI. Why Language Models Miss Retraction Signals The technical architecture of most AI research assistants makes them inherently vulnerable to the retraction problem. Understanding why helps explain what solutions might actually work. Corpus quality controls lacking AI models learn from their training corpus, the massive collection of text they analyse during development. Most organisations building these models prioritise breadth over curation, scraping academic databases, preprint servers, and publisher websites without rigorous quality checks. The assumption is that more data produces better models, but this approach treats all papers equally regardless of retraction status. Even when training data includes retraction notices, the AI might not recognise them as signals to discount the paper's content. A retraction notice is just another piece of text unless the model has been specifically trained to understand its significance. Sparse or inconsistent metadata Publishers handle retractions differently, creating inconsistencies that confuse automated systems: Some journals add "RETRACTED" to article titles Others publish separate retraction notices A few quietly remove papers entirely This lack of standardisation means AI systems trained to recognise one retraction format might miss others completely. Metadata، the structured information describing each paper, often fails to consistently flag retraction status across databases. A paper retracted in PubMed might still appear without warning in other indexes that AI training pipelines access. Hallucination and overconfidence AI hallucination occurs when models generate plausible-sounding but false information, and it exacerbates the retraction problem. Even if a model has no information about a topic, it might confidently fabricate citations or misremember details from its training data. This overconfidence means AI assistants rarely express uncertainty about the papers they recommend, leaving users with no indication that additional verification is needed. Real-Time Retraction Data Sources Researchers Should Trust While AI tools struggle with retractions, several authoritative databases exist for manual verification. Researchers concerned about citation integrity can cross-reference their sources against these resources. Retraction Watch Database Retraction Watch operates as an independent watchdog, tracking retractions across all academic disciplines and publishers. Their freely accessible database includes detailed explanations of why papers were withdrawn, from honest error to fraud. The organisation's blog also provides context about patterns in retractions and systemic issues in scholarly publishing. Crossref metadata service Crossref maintains the infrastructure that assigns DOIs to scholarly works, and publishers report retractions through this system. While coverage depends on publishers properly flagging retractions, Crossref offers a comprehensive view across multiple disciplines and publication types. Their API allows developers to build tools that automatically check retraction status, a capability that forward-thinking platforms are beginning to implement. PubMed retracted publication tag For medical and life sciences research, PubMed provides reliable retraction flagging with daily updates. The National Library of Medicine maintains this database with rigorous quality control, ensuring retracted papers receive prominent warning labels. However, this coverage is limited to biomedical literature, leaving researchers in other fields without equivalent resources. DatabaseCoverageUpdate SpeedAccessRetraction WatchAll disciplinesReal-timeFreeCrossrefPublisher-reportedVariableFree APIPubMedMedical/life sciencesDailyFree Responsible AI Starts with Licensing When AI systems access research papers, articles, or datasets, authors and publishers have legal and ethical rights that need protection. Ignoring these rights can undermine the sustainability of the research ecosystem and diminish trust between researchers and technology providers. One of the biggest reasons AI tools get it wrong is that they often cite retracted papers as if they’re still valid. When an article is retracted, e.g. due to peer review process not being conducted properly or failing to meet established standards, most AI systems don’t know, it simply remains part of their training data. This is where licensing plays a crucial role. Licensed data ensures that AI systems are connected to the right sources, continuously updated with accurate, publisher-verified information. It’s the foundation for what platforms like Zendy aim to achieve: making sure the content is clean and trustworthy. Licensing ensures that content is used responsibly. Proper agreements between AI companies and copyright holders allow AI systems to access material legally while providing attribution and, when appropriate, compensation. This is especially important when AI tools generate insights or summaries that are distributed at scale, potentially creating value for commercial platforms without benefiting the sources of the content. in conclusion, consent-driven licensing helps build trust. Publishers and authors can choose whether and how their work is incorporated into AI systems, ensuring that content is included only when rights are respected. Advanced AI platforms, such as Zendy, can even track which licensed sources contributed to a particular output, providing accountability and a foundation for equitable revenue sharing. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
5 Tools Every Librarian Should Know in 2025
The role of librarians has always been about connecting people with knowledge. But in 2025, with so much information floating around online, the challenge isn’t access, it’s sorting through the noise and finding what really matters. This is where AI for libraries is starting to make a difference. Here are five that are worth keeping in your back pocket this year. 1. Zendy Zendy is a one-stop AI-powered research library that blends open access with subscription-based resources. Instead of juggling multiple platforms, librarians can point students and researchers to one place where they’ll find academic articles, reports, and AI tools to help with research discovery and literature review. With its growing use of AI for libraries, Zendy makes it easier to summarise research, highlight key ideas, and support literature reviews without adding to the librarian’s workload. 2. LibGuides Still one of the most practical tools for librarians, LibGuides makes it easy to create tailored resource guides for courses, programs, or specific assignments. Whether you’re curating resources for first-year students or putting together a subject guide for advanced research, it helps librarians stay organised while keeping information accessible to learners. 3. OpenRefine Cleaning up messy data is nobody’s favourite job, but it’s a reality when working with bibliographic records or digital archives. OpenRefine is like a spreadsheet, but with superpowers, it can quickly detect duplicates, fix formatting issues, and make large datasets more manageable. For librarians working in cataloguing or digital collections, it saves hours of tedious work. 4. PressReader Library patrons aren’t just looking for academic content; they often want newspapers, magazines, and general reading material too. PressReader gives libraries a simple way to provide access to thousands of publications from around the world. It’s especially valuable in public libraries or institutions with international communities. 5. OCLC WorldShare Managing collections and sharing resources across institutions is a constant task. OCLC WorldShare helps libraries handle cataloguing, interlibrary loans, and metadata management. It’s not flashy, but it makes collaboration between libraries smoother and ensures that resources don’t sit unused when another community could benefit from them. Final thought The tools above aren’t just about technology, they’re about making everyday library work more practical. Whether it’s curating resources with Zendy, cleaning data with OpenRefine, or sharing collections through WorldShare, these platforms help librarians do what they do best: guide people toward knowledge that matters. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
Balancing AI Efficiency with Human Expertise in Libraries
AI in libraries is making some tasks quicker and less repetitive. However, even with these advances, there’s something irreplaceable about a librarian’s judgment and care. The real question isn’t whether AI will take over libraries, it’s how both AI and librarians can work side by side. How AI Helps in Libraries According to Clarivate Pulse of the Library 2025 survey, among 2,000 academic library professionals globally, many said they don’t have enough time or budget to learn new tools or skills, a challenge made even harder as global digital content is projected to double every two years. Here’s where AI tools for librarians prove useful: Cataloguing: AI can scan metadata and suggest subject tags in minutes. Search: Smarter search systems help students and researchers find relevant materials without digging through dozens of irrelevant results. Day-to-day tasks: Think overdue notices, compiling basic reading lists, or identifying key sources and trends to support literature reviews. This is where library automation with AI comes in handy. Instead of replacing people, these tools free up time. A librarian who doesn’t have to spend hours sorting through data can focus on supporting students, curating collections, analysing usage statistics to make informed decisions or tracking resource usage against budgets. Where Human Expertise Still Matters AI is fast, but it’s not thoughtful. A student asking, “I’m researching migration patterns in 19th-century Europe, where do I start?” gets much more from a librarian than from a search algorithm. Librarians bring context, empathy, and critical thinking that machines can’t replicate. This is why human-AI collaboration in libraries makes sense. AI takes care of the routine. Humans bring the nuance. Together, they cover ground neither could manage alone. Finding the Balance So how do libraries get this balance right? A few ideas: Think of AI as a helper – not a replacement for staff. Invest in training – librarians need to feel confident using AI tools and knowing when not to rely on them. Keep the focus on people – the goal isn’t efficiency for its own sake, it’s about better service for students, researchers, and communities. Final Thoughts By using AI to handle routine administrative tasks like cataloguing, managing records, or tracking resource usage, librarians free up time to focus on the part of the job that drew them to this profession in the first place: supporting researchers and students, curating meaningful collections, and fostering learning. Combining the efficiency of AI in libraries with the expertise of librarians creates a future where technology supports the human side of education. .wp-block-image img { max-width: 85% !important; margin-left: auto !important; margin-right: auto !important; }
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