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Understanding Citation Styles: Your Pocket Guide to Citing Academic Sources

Apr 4, 2023 |

14 Mins Read

One of the most defining aspects of academic research writing is a demonstration of research-oriented findings. Referring to credible sources and data is made possible through the concept of citations. In this blog, we’ll equip you with citation knowledge across multiple different formats and aid in understanding the purpose of each one.

The purpose of citations

When you search the definition of the term ‘citation’ on google, you’re met with the following description: a quotation from or reference to a book, paper, or author, especially in a scholarly work. Citing sources has multiple purposes, the primary focus is to give credit to the original author. When writing a specialised research paper, the author will cite external sources when referencing the material in their own work to establish strength, transparency and authority within their research.

This positions their work in a specific context to depict their stance in the larger discussion. Citations also serve as an efficient way to provide references to others wanting to explore the subject or even use them within their own academic research papers. Overall, citing establishes an important roadmap in the research process.

Why are there different types of citations?

Different citation formats are utilised across different disciplines. While it might be convenient to have one universal citation format, it is not possible because different fields focus on unique information within their respective research; this requires citation formats to be tailored to the field’s primary focus.

The APA (American Psychological Association) citation format is utilised in social sciences like psychology, sociology, anthropology as well as education. Moreover, the MLA (Modern Language Association) citation format is largely used within humanities and the Chicago citation format is applied in the fields of Business, History & Fine Arts. Finally, the Harvard citation format is primarily used in education.

Citing in APA Referencing Format

APA (American Psychological Association) referencing style is a widely used referencing style in social science disciplines, such as psychology, sociology, and education. The style is characterised by the use of in-text citations, which typically include the author's last name and the year of publication of the source being cited.

The APA style requires a detailed reference list, which includes all sources referenced in the text. The reference list is arranged alphabetically by the author's last name, and each entry provides comprehensive information about the source, including the title, publisher, and publication date.

Book: Author, A.A. (Year of Publication). The Title of work. Publisher City, State: Publisher.
Journal Article: Last name, Initials. (Year). Article title. Journal Name, Volume(Issue), Page range. DOI or URL
Magazine: Author, A.A. (Year, a month of Publication). Article title. Magazine Title, Volume(Issue), pp.-pp.
Newspaper: Author, A.A. (Year, Month Date of Publication). Article title. Magazine Title, pp. xx-xx.
Website: Author, A.A. (Year, Month Date of Publication). Article title. Retrieved from URL

Citing in MLA Referencing Format

MLA (Modern Language Association) referencing style is a popular method of citing sources used in academic writing, particularly in the humanities. The style is characterised by the use of in-text citations, which typically include the author's last name and the page number(s) of the source being cited. The MLA style also requires a comprehensive list of Works Cited at the end of the document, which includes all sources referenced in the text. The Works Cited page is arranged alphabetically by the author's last name or, if there is no author, by the first word of the title.

Book: Last Name, First Name. Book Title. Publisher City: Publisher Name, Year Published. Medium.
Journal Article: Author last name, First name. “Article Title.” Journal Name, vol. Volume, no. Issue, Month Year, Page range. DOI or URL.
Magazine: Last Name, First Name. “Article Title.” Magazine Name Publication Date: Page Numbers. Medium.
Newspaper: Last Name, First Name. “Article Title.” Newspaper Name Publication Date: Page Numbers. Medium.
Website: Last Name, First Name. “Page Title.” Website Title.Sponsoring Institution/Publisher. Publication Date: Page Numbers. Medium.

Citing in Harvard Referencing Format

Harvard style referencing, also known as author-date referencing, is a widely used referencing system that originated from Harvard University. It is a method of acknowledging sources of information in academic writing, by citing the author's last name and the year of publication in the text. Harvard referencing style also requires a detailed list of references at the end of the document, arranged alphabetically by the author's last name, which includes all the sources cited in the text. This style of referencing is used in many disciplines, including the social sciences, humanities, and natural sciences.

Journal Articles: Surname, Initial. (Year of publication) 'Title of article', Title of Journal, volume number (issue number), page reference. If accessed online: Available at: DOI or URL (if required) (Accessed: date).
Books: Surname, Initial. (Year of publication) Title. Edition if later than first. Place of publication: publisher. Series and volume number if relevant.
Newspaper Article: Surname, Initial. (Year of publication) 'Title of article', Title of Newspaper, Day and month, Page reference.
Online Newspaper Article: Surname, Initial. (Year of publication) 'Title of article', Title of Newspaper, Day and month, Page reference if available. Available at: URL (Accessed: date).
Website: Surname, Initial. (Year that the site was published/last updated) Title of web page. Available at: URL (Accessed: date).

Citing in Chicago Referencing Format

The Chicago referencing format is commonly used in the fields of Business, History and Fine Arts. It offers two main citation styles: the notes and bibliography style and the author-date style.

The notes and bibliography style involves the use of footnotes to provide brief citations within the text and a corresponding bibliography at the end of the document. In contrast, the author-date style involves in-text citations that include the author's last name and the date of publication. A comprehensive reference list is also required at the end of the document.

Journal Article: Author last name, First name. “Article Title.” Journal Name Volume, no. Issue (Month or Season Year): Page range. DOI or URL.
Book: Last Name, First Name. Title of Book. Publisher City: Publisher Name, Year Published.
Magazine: Last Name, First Name. Article title. Magazine Title, Month Date, Year of publication.
Newspaper: Last Name, First Name. “Article Title.” Newspaper Name, Publication Date.
Website: Last Name, First Name. “Page Title.” Website Title. Web Address (retrieved Date Accessed).

Citations and referencing bring attention to details within each format. While it is a time-consuming section to fulfill in an academic paper, it’s also an important skill to have as a researcher, to be able to dissect other research papers and build an authoritative and strengthened academic paper with your own research. Understanding which citation format is best suited for your discipline is equally important; this citation pocket guide covered the 4 commonly utilised citation styles which are APA, MLA, Harvard and Chicago.

Discover an array of academic resources now on Zendy where you can read through research worry-free because we offer automatic citations across all our books, journal articles, proceedings and more.

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; }

Oct 1, 20255 Mins ReadDiscover

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; }

Oct 1, 20256 Mins ReadDiscover

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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