Laws and regulations are ever-changing parts of our societies, adapting to societal needs and changing perspectives. For instance, environmental regulations have increased significantly over the last century, defining more actions that are harmful to the environment as deviant than before (Katz et all, p. 8). Likewise, being a member of the LGBTQ+ community is regarded as criminal behaviour by law in some places of the world, whereas other places have accepted these individuals (Saez, p. 258-259). These examples illustrate that what is defined as ‘bad’ behaviour is subjective. The same is true for what is regarded as academic deviance in academic institutions. Different views do not only vary across locations and times but also between individuals in these institutions.
The researcher McCabe (2012) discovered that 64% of students admitted to cheating on a test, 58% admitted to plagiarism and a shocking number of 95% admitted to participating in some level of academic dishonesty. Given the frequency of academic cheating, it is relevant to see how students who do and students who do not engage in academic cheating differ in how they each view ‘bad’ behaviour in academia. Statistics of a questionnaire filled out by first-year students following the course sociology at University College Cork observe how the perspectives of students who engage in academic cheating, representing 48%, and those who do not cheat, the other 52%, differ on matters regarding academic deviance. This essay seeks to explore how views on academic dishonesty and its effect vary, confirming that bad behaviour is in the eye of the beholder. This will be discussed through relevant topics including, deviance as a social construct, labelling theory, stigmatization, constructive shaming and neutralization.
Deviance as a social construct;
What is defined as bad behaviour is a result of social constructs that are shaped differently depending on the time and place. These constructs include aspects like power dynamics, political and ethical ideas, and societal norms (Holmes, 1881, p.1). In the issue of academic deviance, a relevant example of this is the fast-developing technologies, forcing academic institutions to reassess their practices around cheating. Not that long ago no rules were necessary to prevent students from using software to improve their academic records. However, nowadays it is a common topic, as platforms such as ChatGPT create an opportunity to cheat with little effort (Sabzalieva & Valentini, 2023, p. 11). Additionally, opinions on how severe academic deviance should be regarded also vary from person to person. Focusing on ChatGPT, 20,9% of all people who have never engaged in academic cheating think that ChatGPT and Grammarly are on the same spectrum of cheating to some degree, while only 10,6% of academically dishonest people agree with this statement. (Survey, 2024) This shows that deviance in academic surroundings is observed differently by different groups of people, depending on their own experiences and formed norms. Thus, the varying perceptions and evolvements of what constitutes as academic deviance show that defining bad behaviour is for a great part subjective.
Neutralization;
Additionally, instead of focusing on how others see the deviant act, it’s important to take into account how the one participating in academic cheating perceives their own behaviour. Often people who engage in bad behaviour try to neutralize the severity in several different ways (Matza, 1964). Looking at the statistics, students who engaged in academic cheating would slightly less often agree that there is a huge difference between different forms of cheating. 29,2% of people who did not cheat agreed to some level that paying for private tuition is just as bad as buying an essay online, while only 21,4% of those who have engaged in academic deviance agreed. Similarly, ChatGPT and Grammarly are considered to be on the same level, with 20,9% of people who have not engage in academic cheating compared to only 11,6% of the people who have. (Survey, 2024) A possible explanation could be that students who engage in academic cheating try to neutralize their behaviour through denial of injury (Matza, 1964). They try to downplay the severity of their deviance by using arguments such as that they are only using Grammarly, which is not as bad as ChatGPT. The way students possibly try to justify their academic deviance by using neutralizations shows that students try to change their view on what constitutes as bad behaviour by using neutralizations to justify their deviance.
Labelling theory;
Not only do the general societal opinions around academic deviance matter but also how students are labelled and treated by society based on certain acts. The labelling theory can help to understand this. First, an act of deviance is carried out, such as academic cheating. This is possibly followed by a reaction. If this reaction does happen you are now labelled as a cheater. This will probably lead to role engulfment, as you accept the label and act accordingly in the future. As you have already been caught cheating it has become part of your master status and people will regard you as a cheater. This can lead to a self-fulfilling prophecy where students engage in academic dishonesty again, referred to as secondary deviance because they have already been labelled as cheaters (Windle et all, 2023, p. 143-145) (Becker, 1966, p. 25-39). Looking at the statistics it shows that 19% of students believe that there would be secondary deviance if a student is caught cheating by the university and suffers a significant punishment. These numbers do not seem high, but considering that the goal of these punishments is to avert people from cheating it does show that some students believe, or maybe even have seen or experienced, that a label as a cheater increases the likelihood of cheating again. (Survey, 2024) This framework of the labelling theory applied to academic deviance shows that actively defining students as cheaters possibly only leads to students viewing themselves as cheaters, leading to more secondary deviance.
Stigmatization and shaming;
Stigma was described by Goffman (1963) as an indecent in which a certain individual is rejected by society as a result of a certain attribute or act, such as academic deviance. How bad this stigmatization is depends on how bad others consider the act of deviance to be (Goffman, 1963). The statistics show that people who have engaged in academic cheating are 12,5% more likely to believe that it is not at all stigmatizing to get caught, compared to only 6,3% of students who have not engaged in such behaviour believing that. Similarly, of students engaging in academic cheating only 4,2% believe that it will mark them for life, while 14,6% believe it will immediately be put in the past. This is a large difference with others who do not cheat, as 12,2% of them believe it will mark them for life and only 2% can believe it would immediately be disregarded. (Survey, 2024) This can probably be explained as students who are not academically dishonest are more likely to fear stigmatization, resulting in their compliance and conformity. The different levels of expected stigmatization show how the different groups perceive how stigmatizing deviant academic behaviour is differently, confirming that ‘bad’ behaviour is in the eye of the beholder.
Constructive shaming;
Perspectives on how to effectively help students, instead of labelling them also differ. Constructive shaming is used to try to find a way to allow the guilty individuals to express regret, allow them to learn from the experience and the effects of their deviance, but not attach a label to their name. It could allow academic institutions to effectively change the students view on what constitutes as bad behaviour in academia. This method still relies on shaming to police norms but tries to avoid secondary deviance that can tag along with stigmatizing labels. (Walgrave & Aertsen, 1996, p. 70-75) Students were quite divided on an idea to implement a certain method of constructive shaming: ‘Following being caught cheating, a meeting will be arranged between the accused, the lecturer, a classmate, and a family member.’ In total 57% liked this idea, while the others did not. It is noteworthy that there was barely a difference in how students judged this method between people who did and did not engage in academic cheating. (Survey, 2024) Interestingly, people with a history of academic deviance would agree with such a method, as based on their actions they do not seem to take the effects of their deviance. However, it could be possible that the part that agreed would like an opportunity to relieve some of the guilt and learn to improve their behaviour, without getting stigmatized. The different views towards this method of constructive shaming show that students are quite divided on its effectiveness, however a small majority is probably open to reshape their views on how to define academically dishonest behaviour.
Conclusion;
In conclusion, our analysis around academic cheating shows that what students consider as bad behaviour can vary and is indeed in the eye of the beholder. Through the framework of social constructivism, it is evident that new developments in society, such as technology, can redefine what is considered “bad” behaviour in the academic environment. Neutralisation shows how people view their own deviant behaviour, and try to justify it in several ways. Furthermore, the labelling theory reveals the impact of attaching labels and shows how it can lead to secondary deviance, as people perceive themselves as cheaters. It becomes clear that fear of stigmatisation can play a role in how likely someone is to engage in academic cheating. On the other hand is the idea of constructive shaming a way to not only change students behaviours, but also how they perceive academic dishonesty. The discussion related to academic deviance points out the subjectivism of defining “bad” behaviour. It encourages to move away from stigmatisation and labelling to punish deviant behaviour and move towards a more constructive method.
Sources
- Becker, H. S. (1963) Outsiders. Studies in the Sociology of Deviance.
- Goffman, E. (1963) Stigma, Notes on the Management of Spoiled Identity. Penguin Books. Available at: https://cdn.penguin.co.uk/dam-assets/books/9780241548011/9780241548011-sample.pdf
- Holmes, O. W. (1881) The Common Law. Little, Brown, and Company. Available at: https://www.jstor.org/stable/j.ctt13x0kkk
- Katz, D. M. et all (2020) Complex societies and the growth of the law. Scientific reports. Available at: https://www.nature.com/articles/s41598-020-73623-x
- Matza, D. (1964) Delinquency and Drift.
- McCabe, D. (2012) Cheating in College: Why Students Do It and What Educators Can Do about It. JHU press. Available at: https://books.google.ie/books/about/Cheating_in_College.html?id=DfBLxVIq5IAC&redir_esc=y
- Sabzalieva, E. & Valentini, A. (2023) ChatGPT and Artificial Intelligence in higher education. UNESCO. Available at: https://www.iesalc.unesco.org/wp-content/uploads/2023/04/ChatGPT-and-Artificial-Intelligence-in-higher-education-Quick-Start-guide_EN_FINAL.pdf
- Saez, M. (2016) The Global Struggle for LGBTQ Rights: Legal, Political, and Social Dimensions. American University Washington College of Law. Available at: https://digitalcommons.wcl.american.edu/facsch_lawrev/1770/
- Walgrave, L. & Aertsen, I. (1996) Reintegrative shaming and restorative justice. European Journal on Criminal Policy and Research. Available at: https://link.springer.com/article/10.1007/BF02736714
- Windle, J. et all. (2023) Criminology, Crime and Justice in Ireland An Introduction. Routledge. Available at: https://www-taylorfrancis-com.ucc.idm.oclc.org/books/mono/10.4324/9781003044284/criminology-crime-justice-ireland-james-windle-maggie-neill-orla-lynch-james-cuffe-fiona-donson-kevin-sweeney
Attachments
- Survey (2024) Deviance is in the Eye of the Beholder Charts and Tables. https://uccireland-my.sharepoint.com/:w:/r/personal/123723309_umail_ucc_ie/Documents/Deviance%20is%20in%20the%20Eye%20of%20the%20Beholder%20Charts%20and%20Tables%20(3).docx?d=w861fb9cc106c4ec1ae82de3575c253bc&csf=1&web=1&e=gU5xyG