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24 March 2024: Some ChatGPT bugs and workarounds

There are two significant risks you face regarding the quality of content you get from ChatGPT.

1. Hallucination. It sometimes just makes things up. This is particularly frustrating when you give it the information and it digresses and makes things up that were not in the source information. For example, we gave it some meeting notes, it went on a digression about N-P completeness in computer science.

2. Memory errors. OpenAI added a memory feature so that it refers to previous questions you have asked. However, the memory feature that they have added makes many assumptions and subsequently causes errors. We have noticed that it performs two mistakes:

• Recites what it gave you previously, even if the topic was utterly different. For example, we gave it some meeting notes to summarise and it produced some Python code that we had asked for last year. Clearly that’s not relevant.
• Recites what it gave you previously, because the content was similar, in its opinion. We created a new chat, gave it the meeting notes, and it gave us the PREVIOUS summary of the previous set of meeting notes. In one case it even produced the summary of a previous request which was on a completely different topic.

Workarounds:

We have found that it’s important to read what ChatGPT produces very carefully and compare it to what you expected. For example, if you give it meeting notes to summarise, rather create a new chat to summarise the next batch. Also, turn off the memory feature and delete what it knows about you (go into settings and under memory there’s an option to delete its memory). It’s better to make it recreate everything every time. Otherwise it becomes repetitive and irrelevant to the current task.

13 June 2023: Generative AI – other services

This is the second of two posts on Generative AI.

Late last year (November), OpenAI released ChatGPT to the public. However, it took a few months for the internet at large to catch on and realise the potential of this new tool. The trend started to take off in December, and by February everyone was talking about it.

ChatGPT is an example of a “Generative AI”. To recapitulate, let’s see what this means. For convenience, let’s call AIs that are not generative, “regular” AIs.

• Regular or “Narrow” AI (Artificial Intelligence): Regular AI, often referred to as “narrow” or “specific” AI, focuses on building systems that can perform specific tasks or solve specific problems. These AI systems are designed to operate within predefined boundaries and excel at specialised tasks. For example, a regular AI could be created to classify images, play chess, or process natural language. Another example is the AI that chooses your words in predictive text. Narrow or regular AI is typically trained using supervised or unsupervised learning techniques and relies on large datasets for training. It aims to optimise performance on a specific task and often follows a rule-based or algorithmic approach. Supervised learning means that a human monitors and/or controls the learning process. Unsupervised means that the computer is left alone to do its own thing.
• Generative AI: Generative AI, on the other hand, refers to a class of AI models that are capable of generating new content, such as images, text, or even music. Generative AI models, like ChatGPT, are trained to learn patterns and relationships in data and generate new samples that are similar to the training data. These models use techniques like deep learning and neural networks to generate content by capturing statistical dependencies and structures in the data they have been trained on. They can create new and original outputs based on learned patterns, and their ability to generate realistic content has improved significantly in recent years.
• A GAN, or Generative Adversarial Network. This is a system which generates new content by setting two systems to compete (adversaries). Imagine there are two artists in a competition: a forger and a detective. The forger’s job is to create fake artwork, while the detective’s job is to identify which artworks are fake and which are real. They both learn and improve through the competition. In the case of a GAN, the forger is called the “generator,” and the detective is called the “discriminator.” The generator’s job is to create fake data, such as images or text, that look as realistic as possible. The discriminator’s job is to examine this data and determine if it’s real or fake. The generator and discriminator go through this process repeatedly, each learning from the other’s successes and failures. Over time, the generator becomes more skilled at creating realistic fake data, while the discriminator becomes better at detecting fakes. In the end, this competition between the generator and discriminator results in the generator being able to create fake data that is very close to the real thing. GANs have been successfully used to generate realistic images, produce synthetic voices, and even generate text that resembles human writing.

Here is a list of useful sites which make use of GAN (generative adversarial network) technology:

7 June 2023: What is the hype around ChatGPT about?

This is the first of two posts on Generative AI.

Late last year (November), OpenAI released ChatGPT to the public. However, it took a few months for the internet at large to catch on and realise the potential of this new tool. The trend started to take off in December, and by February everyone was talking about it.

Well, what is ChatGPT? ChatGPT is an LLM, or Large Language Model, released by the company OpenAI, founded in 2015.

A language model is a program that has been trained on a huge amount of text data, like books, articles, and websites. It learns patterns and relationships between words, so it can predict what words or phrases are likely to come next in a sentence. We see an example of this in predictive text in cellular telephone chat applications.

The “large” part means that the model has been trained on a massive amount of data, which helps it become even better at understanding and generating human-like text. It’s like having read millions of books and articles and being able to recall that information to have conversations.

When you interact with an LLM, you can ask it questions or give it prompts, and it will generate responses that make sense based on what it has learned from its training. It tries to understand the context and provide helpful or informative answers.

Of course, it’s important to remember that while the model can generate text that sounds human, it doesn’t truly understand the meaning behind the words. It’s more like a mimic that tries to match patterns it has seen in the training data.

But why is ChatGPT called a “Generative AI”? The clue to this is the term “generative”, implying creation. For convenience, let’s call AIs that are not generative, “regular” AIs.

• Regular or “Narrow” AI (Artificial Intelligence): Regular AI, often referred to as “narrow” or “specific” AI, focuses on building systems that can perform specific tasks or solve specific problems. These AI systems are designed to operate within predefined boundaries and excel at specialised tasks. For example, a regular AI could be created to classify images, play chess, or process natural language. Another example is the AI that chooses your words in predictive text. Narrow or regular AI is typically trained using supervised or unsupervised learning techniques and relies on large datasets for training. It aims to optimise performance on a specific task and often follows a rule-based or algorithmic approach. Supervised learning means that a human monitors and/or controls the learning process. Unsupervised means that the computer is left alone to do its own thing.
• Generative AI: Generative AI, on the other hand, refers to a class of AI models that are capable of generating new content, such as images, text, or even music. Generative AI models, like ChatGPT, are trained to learn patterns and relationships in data and generate new samples that are similar to the training data. These models use techniques like deep learning and neural networks to generate content by capturing statistical dependencies and structures in the data they have been trained on. They can create new and original outputs based on learned patterns, and their ability to generate realistic content has improved significantly in recent years.

So, in summary, regular AI refers to AI systems designed for specific tasks, while generative AI focuses on models that can generate new content based on learned patterns. Generative AI is a subset of AI that leverages advanced techniques to produce original outputs, while regular AI covers a broader range of AI systems aimed at specific tasks.

ChatGPT has many powerful applications. A lot of educationalists and researchers, particularly in higher education, are concerned that ChatGPT can be used to write paragraphs or even essays, without crediting it, and therefore give a false impression of the competence of the alleged human author. Students might, for example, get ChatGPT to write an essay for them on a topic that they are not clear on, and submit the essay, and receive a good score, even though they themselves did not “deserve” that score. We at SurveyFiesta™ are more optimistic. We see the opportunities presented by ChatGPT as follows:

• Information retrieval: ChatGPT can help researchers and educators find relevant information quickly. You can ask it questions or provide prompts related to your research topic, and it can provide you with relevant facts, explanations, or even suggest further resources to explore.
• Concept exploration: If you’re trying to understand a complex concept, you can use ChatGPT to engage in a dialogue. You can ask it to explain the concept in simpler terms, provide examples, or even compare it to other concepts. This can help deepen your understanding and clarify any confusion.
• Writing assistance: ChatGPT can act as a writing companion for researchers and students. If you’re working on a paper, thesis, or any other written piece, you can use ChatGPT to brainstorm ideas, outline your thoughts, or even get feedback on your writing. It can provide suggestions, help rephrase sentences, or offer alternative wording. For example, you can directly ask it to paraphrase sentences to avoid plagiarism problems.
• Language learning: ChatGPT can be beneficial for language learners. You can engage in conversations with it to practice your writing or speaking skills. It can correct your sentences, offer vocabulary suggestions, or provide explanations when you encounter difficulties with grammar or usage.
• Problem-solving and critical thinking: If you’re facing a complex problem or trying to think through a challenging scenario, ChatGPT can act as a thinking partner. You can discuss the problem, articulate your thoughts, and receive alternative perspectives or ideas. It can help you explore different angles and encourage creative thinking.
• General knowledge exploration: ChatGPT has access to a wide range of information, so you can use it to explore various topics and expand your general knowledge. It can provide historical facts, scientific explanations, or engage in discussions on current events. It can be a handy tool for discovering new areas of interest or staying updated on different subjects.

Note that the current version available on OpenAI’s website, ChatGPT 3.5, does not have internet access, and was only trained on data up until 2021, so its information is out of date. You have to subscribe to access version 4.0, which does have internet access. The updated version also has plugins which perform other tasks.

To avoid accusations of plagiarism when using ChatGPT or any language model, researchers and students should follow these guidelines:

• Understand the model’s limitations: Remember that language models like ChatGPT generate text based on patterns they’ve learned from training data. They don’t have original thoughts or expertise. Acknowledge that the generated content is not your own work but a product of the model. ChatGPT sometimes also experiences “hallucinations”, that is, inappropriate or otherwise “weird” data that makes it say strange things. You therefore have to proofread everything it says because it may contain something offensive or strange.
• Use generated text as a reference: Treat the output from ChatGPT as a source of information and ideas rather than directly copying it. Take the generated text as inspiration and reshape it in your own words to demonstrate your understanding and originality.
• Combine multiple sources: Incorporate information from multiple sources, including the output from ChatGPT, to create a well-rounded and comprehensive piece of work. By including various perspectives and citing relevant sources, you show that your work is a result of thorough research rather than relying solely on the language model.
• Attribute and cite appropriately: If you directly quote or paraphrase any information obtained from ChatGPT or any other source, make sure to attribute it properly and provide citations. Follow the citation style required by your institution or academic guidelines.
• Review and revise: Carefully review the content generated by ChatGPT and ensure that it aligns with your own writing style, knowledge, and understanding. Revise and edit the text to incorporate your own insights and ideas, making it a genuine reflection of your work.
• Seek human feedback: Consult with your peers, instructors, or mentors to get feedback on your work. Discuss the ideas generated by ChatPT and seek input on how to integrate them appropriately while maintaining academic integrity.

By following these guidelines and demonstrating your understanding and originality in your work, you can use ChatGPT as a helpful tool while avoiding plagiarism concerns. Remember, responsible and ethical use of technology is crucial in academic and research settings.

If you are skeptical of the power of LLMs and/or ChatGPT at this stage, please note: half of the above article was written by ChatGPT (mostly the bullets).

30 March 2023: New SurveyFiesta™ Features

We have updated SurveyFiesta™ to offer Net Promoter Score features.  Net promoter score is a market research metric that is based on a single survey question asking respondents to rate the likelihood that they would recommend a company, product, or a service to a friend or colleague. (Wikipedia definition).

30 January 2022: New SurveyFiesta™ Features

We have updated SurveyFiesta™ to include features specifically aimed at academics. We now offer:

• Mark allocation (scoring) for most question types;
• Likert scale question types (grids of multiple or single choice);
• Two-column (sort terminology);
• Rating value (similar to star rating);
• Numeric grids (to rate properties in a grid, e.g. rows of quality controls and then columns of measures of acceptability levels of those quality controls);
• Image maps which let you identify objects in an image, e.g. to label parts in a biology test;
• File upload (for submission of work); and
• Demographics (to capture student details in tests, for example).

30 January 2021: Fake news and fake research

Since the start of the Covid-19 pandemic there has been a lot of fake news being circulated online, in social media, and even some fake research. It is important therefore to be able to tell which online content is legitimate.

How to tell if something is fake news

We present a convenient rubric to tell if something is fake news:

• It features poor-quality or grainy image/s (to hide the tampering with the image).
• It is sensationalist, scary and/or makes you angry.
• It does not come from a reputable TV channel, reputable newspaper, or an actual person in a position of authority. If you go to reputable newspaper websites there is no mention of it.
• It is not compatible with democratic laws, or a democratic constitution. For example, in most of the world, banning communication is unconstitutional, so anything predicting that such will happen, must be considered suspicious.
• The government has not announced it. Of course, this depends on which country you’re in as to how much you want to trust the government; some governments are repressive. Certainly in South Africa, and other countries run under a constitutional democracy, bans on free speech should not occur.
• The article or post or meme confirms your beliefs about other conspiracies. If it confirms for example that there’s a global plot run by lizard aliens, it is fake news.
• If it was seen on Twitter, Facebook, Youtube, Whatsapp and any similar social media platform – it is probably fake news. Youtube is particularly heavily implicated in spreading fake news videos.
• Any content which has lots of exclamation marks. You can generally google the content and see if it comes up on reputable sites or is just copy/pasted on random sites that you have never heard of. If the latter, it is fake news.
• It is in plain text – not a PDF, professionally typset. It has spelling or grammar errors (government officials are not allowed to publish items with spelling and grammar errors).
• The website or Youtube video that contains the “information” contains adverts, e.g. for the author’s conspiracy books or videos.  The purpose of sensationalism is to attract customers to buy something. 
• Give the “news” a score based on the above items. If it scores more than 2, you can be reasonably sure that it is fake.
• Pretend that EVERY DAY on internet is April Fool’s day and treat everything with the same suspicion.
• Note that reputable sites, like wikipedia and university websites, do not contain adverts. Fake news is usually designed for a commercial agenda; e.g. to get a politician re-elected (so he can continue to abuse state resources for funding); or, to get you to buy a product. Generally, the more adverts on a website, the less reliable it is.
• In short, unless you saw the information on a government or university website, or wikipedia, or a well-known reliable news source, such as BBC, The Guardian, Al Jazeera, Daily Maverick, etc., please do not share it. Many (BUT NOT ALL) mainstream media or news sources are unreliable or dubious, or prone to sensationalism or exaggeration.
• To check an item, google it using the site directive. For example, if you would prefer to get sober, calm facts, just add site:snopes.com or site:en.wikipedia.org or site:.edu or site:.ac.uk .. and similar domains, you will see much more sober, less hysterical anaylses on those sites.
• Remember, even if a person in a video looks convincing, they could be a deepfake (computer generated video), or a paid actor.

How to tell if something is a fake publication

Fake publications come in three forms: prank publications, predatory publications and just plain bad science. For more on predatory publications and how to identify them, please see our earlier post below from 25 July 2020. An example of an AI that generates prank mathematics publications is here.

On the matter of prank publications, those are generally easier to detect since they make obviously ridiculous claims. However, the ability to detect whether the claims in the publication are indeed ridiculous depends on your academic backround. A particularly notorious case is the Sokal Hoax. In brief, a physicist called Alan Sokal sent a nonsense paper to a humanities journal:

“In 1996, Sokal submitted an article to Social Text, an academic journal of postmodern cultural studies. The submission was an experiment to test the journal’s intellectual rigor, and specifically to investigate whether “a leading North American journal of cultural studies—whose editorial collective includes such luminaries as Fredric Jameson and Andrew Ross—[would] publish an article liberally salted with nonsense if (a) it sounded good and (b) it flattered the editors’ ideological preconceptions.”  Unfortunately the article was published. You can read more about it here https://en.wikipedia.org/wiki/Sokal_affair.

This prank demonstrated that academics are prone to Confirmation Bias — that is, that we are inclined to believe more things which are similar to what we already believe, or, which support our existing beliefs. Confirmation Bias is the bias which leads us all to fall prey to fake news and fake science. Most people fall for such things due to some or other cognitive bias. A definitive list of cognitive biases is here. You can obtain a nice poster about biases here.

On the matter of bad science, these are cases where the scientist or researcher has an ulterior motive behind their research. This is why declaring your funding source is so important, as is a literature review. Because unless you show an understanding of the existing literature, and show who is funding your research, it remains suspect. As an example, consider the case of Wakefield, 1998. His article caused hysteria around vaccines which still has not died down, despite the article being retracted.

“The final episode in the saga is the revelation that Wakefield et al.[1] were guilty of deliberate fraud (they picked and chose data that suited their case; they falsified facts).[9] The British Medical Journal has published a series of articles on the exposure of the fraud, which appears to have taken place for financial gain.” (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3136032/)

The same rubric applies as with fake news. If an article (be it from a professor or not), sounds hysterical and incredible, it probably is not worthy of serious consideration. Science is a sober serious business. Anything that is radical should be looked at with very careful scrutiny, and a replication of its experiments should always be performed to validate the findings.

25 July 2020: Publish or Perish vs Predatory Journals

In our current academic context there is a lot of pressure to publish journal articles even if they are not of good quality. This is called the “publish or perish” paradigm. In the early 20th century, many professors only had undergraduate degrees or masters’ degrees, and only a handful of publications. Nowadays, tenure is much harder to obtain and requires dozens if not hundreds of publications. So there is enormous pressure to publish to get promoted.

At SurveyFiesta, we prefer to recommend that researchers do not do humanity a disservice by publishing questionable research or rehashes of old papers just to gain quantity. Let us rather focus on quality.

Predatory journals accept journal articles without peer review. What predatory journals do, is they offer to publish your paper, then accept your paper as-is, then charge “page fees” for publication. They do not review for quality. This lowers the quality of academic work worldwide as researchers who are under pressure to “publish or perish” assume that it is a legitimate offer from a legitimate journal. And when other academics search for content on a topic, they might accidentally download the content from the predatory journal without realising the quality is poor.

Consider for example (we just made this name up as an example) – say, ‘Journal of Business and Economics Studies.’ This journal does not appear on the Department of Higher Education and Training, Science and Technology’s accredited journals list, which you can get from here:

• http://libguides.wits.ac.za/Scholarly_Research_Resources/Accredited_Journals

And here is the list of predatory publishers:

• https://libguides.wits.ac.za/Scholarly_Research_Resources/Predatory_Publishers

If you Google such journals (not on the list), you may find that they are published by for example “Academic Stellar” (not their real name) publishers or something like that. Is its content entirely about economics? No; you will probably see that it has a range of topics. So it is not in fact a real journal. It is a predatory journal.

There are also conferences and book publishers which do this – they approach you to publish your work, because they assume you’re desperate to get published and will therefore pay for it. The following article from a University of Johannesburg academic explains further.

• https://www.pressreader.com/south-africa/business-day/20170927/281668255167320

Here is a South African author who reported this particular journal. As you can see, a trivial google of any journal will show you whether it is legit or not:

• http://econjeff.blogspot.co.za/2010/07/journal-spam.html

And here is a real journal just for comparison:

• https://www.journals.elsevier.com/journal-of-economics-and-business/

See the difference? Look at the work quality, content, and topic adherence. Look at the publisher guidelines. Google the word “scam” with the journal name.

• https://www.nature.com/news/stop-this-waste-of-people-animals-and-money-1.22554
• https://beallslist.weebly.com
• https://darrengoossens.wordpress.com/science-spammers/

We hope the above is helpful. You can find out more about reputedly fake book publishers here:

• https://en.wikipedia.org/wiki/OmniScriptum

In this case, what the publisher does is try to get recently graduated postgraduates to surrender their copyright in order to see their thesis “published” as a book. However, the book, once published, is so expensive that no-one will really read it or buy it, and you obviously have to pay to get it “published”.

25 April 2020: Critical Thinking

• In this time of Covid-19 (Coronavirus, SARS-COV-2), we find that there are many conspiracy theories being circulated. People often re-post news articles or memes online without using any critical thinking. Especially if it appeals to some deeply-held belief or gut feel. Please therefore do the following before you post something online.
• Consider whether the meme or article agrees with your poltiical, racial or religious views? If so, check it carefully, because it may be false.
• Consider whether the meme or article agrees with your lifestyle, dietary views, or views on personal matters, relationships, etc.? If so, check it, it may be false.
• How do you check the legitimacy of a post?
  • Google it. Take a copy and paste of a few sentences from the thing you want to post – e.g. an “interesting” article about how Johns Hopkins says MSG causes cancer. Copy two or three sentences, and paste them into Google. If the first sites that come up are hoaxslayer or snopes.com, you can pretty much bet that it’s a hoax.
  • Go to Jstor.org or Pubmed.com and search for the key words in the claim. E.g. “MSG cancer link”. See what the academics are saying.
  • Search for the same keywords on https://en.wikipedia.org.
  • Reverse image search. If it is an image, crop out the meme words and put it into a reverse image search on Google (go to https://images.google.com and click the small grey camera). It should come up with the original (unedited) image. You can then tell based on where the image came from as to whether it has been edited. Note that this feature appears only on laptops or desktop computers.

• That’s all it takes to not spread misinformation online. Please do this as a matter of course. Just check your facts. Remember “Confirmation Bias“. Just because something makes sense to you, does not mean it is true. Facts are not matters on which you can have an opinion, and they are not culturally relative. Facts are human-independent.
• Legal ramifications. You are not entitled to mislead others by spreading falsities as if they were facts, merely by name-dropping a supposed authority that pronounced them. You have to check whether the cited authority did in fact pronounce these things that you hold to be true, to be facts. For example, if you advocate that people eat dishwashing pods or drink bleach to cure disease, you may be held legally responsible if you make that recommendation in public.

25 February 2020: Academic terminology

1. Conference, congress, colloquium, symposium — a gathering of persons to discuss academic concepts. Larger conferences tend to be called ‘congresses’, smaller ones ‘symposia’. Note the spelling of the plural. If a conference has mini-conferences inside it, those are called symposia. Symposia comes from Greek meaning “to sit together”. Colloquium comes from Latin, meaning “to talk together”. Conference and congress are Latin -derived words as well, meaning To bring together, and To travel together.
2. Abstract — a summary of a piece of academic work. It appears at the beginning of a piece of academic work and summarises the research question, and the answer that the work gives. It is marked with the word “Abstract” at the beginning of the paper. It also often has keywords listed below it that summarise the work or the topic.
3. Literature Review — a piece of work which surveys the existing state of the art or knowledge in a particular research area, usually giving the consensus view or a summary of what the general consensus is, as well as any strong dissenting voices.
4. Research Proposal — a short essay explaining what it is that the author wants to research. It will usually contain a literature review.
5. Poster — a piece of academic work summarised on a large piece of cardboard (literally a poster). It is presented at a conference by the person standing next to their poster, in a hall, and they wait for people to come up to them and talk to them. One way of thinking of this is as a random opportunity to meet someone who is an expert on a particular area of research. A poster is typically displayed for a limited time on a particular day of the conference. An “electronic poster” is when the person keeps their poster electronically on a computer and presents it electronically (i.e. not printed) — but in the same public space, e.g. a hall.
6. Paper — an essay, usually 10-20 pages long, which starts with an abstract, and examines a research question. It usually has an introduction, a main body, in which contrasting ideas are debated, and a conclusion, which usually selects one of the contrasting ideas as the more likely to be correct – or it presents a new idea. If the paper does not present a new idea, it is usually called a literature review, and if it does present a new idea, it is usually called a research paper. A person presenting a paper at a conference will usually have PowerPoint presentation that they will talk about in front of a small audience – usually up to about 100 people at most, but sometimes as few as 1-2 people. This is usually done in a closed room, reserved for the paper in a certain time slot on a certain day.
7. Symposium, Panel — a mini-conference inside a conference, most often, consisting of a panel of experts who sit around and present their papers in turn. The audience, who do not sit around the table, get to listen to their discussion. A symposium is brought together by a ‘convenor’, who often presents the first paper. The symposium or panel is summed up at the end by the ‘discussant’.
8. Plenary — a session or presentation given by an important or famous researcher, usually attended by everyone in a conference. From Latin for “Complete” or “full”, related to the word “plenty”. A plenary is the same as a paper presentation except for the audience size.
9. Session — a type of presentation, most often a synonym for a panel or symposium. If a session has only one presenter, that’s a paper session. If the session has a person showing a poster, that’s a poster session. If it’s a group of people, that’s a panel or a symposium session.
10. Discipline, Research Area — an area of study with specific methods, pre-commitments, and specific foci or areas that it focuses on. A limited area of study. More popular areas of study tend to be broken down into more sub-disciplines or research areas. So for example Physics contains research areas such as quantum mechanics, fluid dynamics, Newtonian mechanics, thermodynamics, astrophysics, nuclear physics, etc.
11. Chair — the head of a subdivision of some kind, e.g. a head of a research area.
12. Reviewer — a person who reads another person’s work to see if it is of acceptable standard.
13. Peer-review — when a piece of academic work is reviewed by someone who is the author’s academic equal (more or less).
14. Blind or anonymous peer review — the process of review taken in most cases, wherein the author does not know who is reviewing their paper, and the reviewer does not know whose paper they are reviewing, to prevent bias in favour of colleagues or friends.
15. Declaration — Reviewers and authors alike are expected to declare their funding interests as well, so that if a paper reviews a product (e.g. tobacco, drugs, etc), or a process (e.g. an industrial process, or a training method, etc.), the reviewer and/or author have to declare if they have financial interests in those products or processes, so that they can be seen to be objective (or biased) as the case may be. Hence, for example, if a pharmaceutical company publishes a paper saying that a certain drug is effective, it makes no sense for the reviewer to be financially involved in that same company, because they will be biased. In short, in academia, it is considered very poor form to “mark your own homework”. Generally you should ensure that whoever “marks your homework” is unbiased and from another organisation. It is even preferable to have a hostile reviewer who has an interest in refuting your work, because if your work passes muster under a hostile review, it is even more respectable.
16. Ethics panel — Some research has or could have ethical problems apart from potential financial corruption. When a researcher proposes to produce some research, through a research proposal, he or she should consider the ethical implications of the research. Will people or animals be harmed by the research, and if so, how? Research which is unethical is not acceptable and would not be permitted unless there is an outstanding (and urgent) rationale behind the research. An Ethics Panel is a body in a research organisation whose job is to ensure that all research done by the organisation complies with international ethics standards.
17. Sub-reviewer — a reviewer who reports to a chair or another reviewer.
18. Editor — a person who reads some writing to check for spelling, grammar and clarity problems. An editor does not check for conceptual or factual problems; that’s what a reviewer does.
19. Journal — a periodical or magazine for academics with articles on academic topics. Most journals cover very specific research areas, and academics submit their papers to those specific journals for peer review. In most cases, work is reviewed by two or three reviewers to ensure there’s a majority in favour of publication, but if there’s a doubt, another reviewer can be called in. Journals have different levels of credibility and some journals are accredited or recognised as leading journals, whereas others are not. For example, getting a publication in Nature is considered a lifetime achievement, whereas getting a publication in the International Journal of Advanced Studies is not considered an achievement. The reason is that some journals are just very picky about what they publish, particularly on content quality and whether the content contributes to the research area; whereas, other publications are not picky at all. For more on this, Google the Sokal Hoax. Some journals are also considered “predatory” – that is, their purpose is to make money, not to advance science and knowledge. More will come up on predatory journals in another blog post.
20. Publish — to get a paper accepted into a journal.
21. PhD, Masters, Thesis, Dissertation — PhD is “doctor of philosophy”; the highest degree you can get (although a “Post Doctorate” was recently introduced). PhD is one level higher than Master. A Master’s degree is typically either by coursework or by “dissertation” (a written discussion of a particular research question). A master’s degree has typically been reviewed by two persons before being awarded the degree. A PhD is typically reviewed by at least three people before being awarded their degree by “thesis”. A thesis is very much like a dissertation — it’s a big book on a particular research topic — except that it offers a new theory, whereas a dissertation does not have to do so. A professorship is not a degree awarded by signing up for a course. A professor, rather, is someone who has published a lot, and who has been awarded the rank by his university.
22. Proceedings — a journal issue containing only papers from a conference. Sometimes with very large conferences, the papers are too long or the conference was only presentations, in which case the Proceedings might contain only the abstracts.
23. Call for Abstracts — the opening phase of a conference wherein the conference organisers send out adverts asking academics to submit abstracts for review by the conference organisers. If the abstracts sent in are accepted, the person may then attend the conference as a presenter.
24. Registration — signing up AND paying your membership/attendance fee for the conference.
25. Delegate — any person at the conference, who may or may not also be a presenter.
26. Invited Speaker — a delegate who was specifically invited to give a plenary or other major session.