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Expected Preparations:
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| Keywords: The components of academic integrity; pitfalls of plagiarism; proper citing; steering clear of academic misconduct; rules for collaboration in this course | ||||
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Objectives:
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Outcomes:
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Deliverables: Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit. Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don’t overlook these. Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page. |
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Evaluation: NA: This unit is not evaluated for course marks. |
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Academic Integrity is a promise that scholars and scientists world-wide give each other, that we will uphold, protect, and promote ethical and practical standards for our work. Its most basic values are proclaimed as honesty, trust, fairness, respect, responsibility, and courage. These are simple ideas, but in order to give them meaning we need to discuss how these values get translated to the details of our everyday work. Unfortunately, this important topic is often compressed to discussing cheating and plagiarism, to managing procedures to detect dishonesty, and to threatening sanctions. It is overlooked that those are just the manifestations of much deeper problems, and focussing on those symptoms alone perpetuates a stereotyped us-versus-them mentality of educators and students alike that is much more likely to make the problem worse than to solve it. The key to counter this lies in a proper understanding of academic integrity as a relational value, and respect as its foundation.
Discussing academic integrity in the abstract is of limited use, the challenge is to put the concepts in practice, in every aspect of this course and this is not a question of behaviour, but of attitude. The attitude needs to be reflected in the choice of teaching materials, in the care in their preparation, in the attitude of impartiality and reproducibility we bring to our experiments, in mutual trust in class, in fairness in assessments, and honesty in assignments. One everyday issue is attribution and we operate a Full Disclosure Policy for attribution in this course. This means everything that is not one’s own, original idea must be identified and properly attributed. Neither I nor you are already perfect in this, but I trust we can come together as a learning community to educate each other and improve.
The rules are simple, but non-negotiable:
To understand why we come up with these rules needs, we need to consider a few basic concepts of ethics,
Why should we base our interactions with others on principles of integrity? The common answer is given in a form of the golden rule5 “Do that to others, which you should wish others to do to you.” – as a rule to organize society by that is simple, and perfectly reasonable.
However there is a problem with this approach, in that it is at its core merely transactional, and Game Theory has shown us that transactional interactions are only stable under very narrow conditions. What the Golden Rule lacks is authentic commitment. However, commitment can’t be obligated, or justified in a transactional framework, because if obligated it is not commitment, but merely deference to authority, and if transacted, it is contingent, and not committed.
We need an alternative account. In 2019, Yi Chen and I developed those ideas in a book chapter on sustainable development (Chen and Steipe (2021)). The core of the argument examined Confucian deontology6 and it turns out that a duty-based argument for ethical_good behaviour is self-contradictory. The same holds for Kant’s categorical imperative - because the “law” is a law of nature, just like gravity: there is no authority that tells you: you are not allowed to float. The obligation arises from realizing the law.
The other day I picked up some fish at St. Lawrence market. The seller made a mistake and did not add one item to my bill. I paid, and left, but noticed the mistake later. So I went back, pointed out the mistake, and paid for the missing amount. The seller was surprised and happy, I felt good.
This is where we need to look for the foundations of academic integrity:
Seeing the seller glad made me glad too, not in a transactional way, but from empathy, and an understanding that at that moment the world had become in a very small way a better place. Being able to do this enhanced my feeling of self-worth, and self-respect.
Such self-respect provides a foundation for non-transactional ethics. Doing the right thing is valuable in and of it self, not just because you might be punished otherwise, or you might wish that no one cheats you in turn, but because it makes your entire world better.
You need to understand where we come from on that, and what this means in detail. So read on. Make sure you understand, and make these ideas your own.
We live in an era of open source, boundless mashup, awesome reposts and instant repurposing of information. It might seem that our rules of referencing and citation are just another academic anachronism. After all, we all copy from stack overflow, right?
No - actually: wrong for two reasons. One: information is not any longer a commodity that increases in value if its supply is artificially constrained. Rather the value of information in academia - our common currency - is now “mindshare”, and mindshare cannot grow without attribution. And two: part of any course at UofT is its “summative assessment”: we mark submissions to evaluate the aptitude and achievements of students. That can only be done if original thinking by the student is clearly identified, and distinguished from merely repeating other’s thoughts.
But let’s face it: UofT has a plagiarism problem. This has gotten worse over the past years - and it seems worse in the CS realms than in the domains of the life sciences. Yet, even if all your peers think no one cares about missing attributions, that doesn’t make it somehow right: ethics is not a result of opinion polls. No matter how socially acceptable plagiarism has become, no matter how many others do “it”, no matter how many likes or upvotes or retweets a “No Big Deal!” post attracts, unethical behaviour is wrong. It goes against everything we stand for as scientists. And it is corrosive, not just for your community, but first and foremost for yourself.
In this course we operate a Full Disclosure Policy. That doesn’t mean you can’t get good data and examples from wherever you find them - on the contrary I absolutely expect you to hunt everywhere for information and examples: biostars, stack, RBloggers, even reddit (sometimes). There is great value in finding how others have addressed a problem, or divide up and organize a particular topic, and compiling the knowledge of the entire community is a great starting point for excellent work. But (a) this process has to be transparent, and, indeed you need to develop and document a sense of pride in mastering this art and attributing the contributions of your sources, and (b) compiling information does not substitute for your understanding of the material that you are presenting.
Note that paraphrasing does not get you around the need to cite and reference. There is nothing wrong with quoting material outright (i.e. copy/pasting and citing the source); paraphrasing is only useful if the original idea needs to be re-expressed for the flow of your argument.
Regardless whether you are reusing, quoting, paraphrasing, summarizing or following someone else’s structure or advice: cite the source and reference it. You can never reference too much, but if you don’t reference enough you are probably committing an academic offence and I am obliged by University Policy to take the issue to the Office of Student Academic Integrity (OSAI). Regardless whether you are writing an assignment, updating your journal, uploading code, replying to posts on the mailing list - for anything that is submitted for credit, directly or indirectly: make sure all your references are complete.
The principle is quite simple:
Full disclosure policy for this course:
If something is not your own, original, new idea, it has a source.
All sources must be referenced.
You probably have seen resources that refer to other’s observations or opinions, and teach you to reference in manuscripts and essays in the life sciences and the humanities. These are generally less relevant for the kind of work that we do, and perhaps this is one of the reasons for poor uptake. Indeed, most of the writing in our courses is informal, and it may not be obvious how to properly embed citations in the flow of the narrative.
The solution is to thouroughly contextualize your attributions with statements such as:
etc. as appropriate.
Some specific points to consider:
A second mistake that has brought students to the Dean’s office more than once is re-use of material from previous courses. This is a simple one: you can’t get academic credit for the same material twice. This means: if you have already submitted something for a different course elsewhere, or for a different assessment in the same course, it is no longer an original contribution. Of course you can cite your own work and then reuse it - if it’s useful, bravo - good for you. But you have to be upfront about it, and apply the Full Disclosure Policy in spirit. Again: if in doubt, ask for advice.
It sometimes happens that a piece of code you are submitting won’t run. It just won’t. You can’t see the mistake, it’s three in the morning, and you just can’t take it anymore. It’s just a small variation from the spec – and you can easily fix the output by hand.
So you edit a few lines in a printout, or a few cells in a spreadsheet, and submit that result. All good, right?
No. Not good at all. You have just falsified your code output. In terms of academic misconduct this is called “concoction”. And it’s pretty high on the list of things that will make for a very bad day.
Do not ever change code output by hand. If an assignment asks you to submit code and results, the exact code you submit must generate the exact output that is claimed for it. Obvioulsy, there may be assessment scripts that will verify that. And when the assessment script signals a discrepancy, that will set off a process …
Above, I’ve highlighted a few issues that I have come accross in past courses. Below, are extensive resources that will help you work better. Go and read them.
Task…
Visit the following sites and read the material carefully:
Then - for your own reference - put a model of the following three types of references into your journal:
If in doubt, ask! If anything about this contents is not clear to you, do not proceed but ask for clarification. If you have ideas about how to make this material better, let’s hear them. We are aiming to compile a list of FAQs for all learning units, and your contributions will count towards your participation marks.
Improve this page! If you have questions or comments, please post them on the Quercus Discussion board with a subject line that includes the name of the unit.
[END]
Or: Do not fabricate results.↩︎
Or: Do not resubmit previous assignments.↩︎
This includes journal articles and books, obviously, but also blogs, discussions on StackOverflow, Github gists, and especially course material of this and other courses, your peer’s notes and journal entries. and material you have produced previously.↩︎
Or: Do not copy anything without attributing its source. The word anything explicitly includes the R code and other writing supplied with your course materials!↩︎
Some form of the golden rule is found in nearly every ethical tradition, for example in the Analects of Confucius (15.24) or in Plato’s Laws (XI.913a). ↩︎
Deontology: an ethics based on the duty to adhere to rules.↩︎