���Ҵ�ý

The teacher-student relationship is a professional one, built on expectations of mutual respect. It also bears mutual responsibilities. For the teacher, this involves preparation for classes and other meetings, establishing a clearly defined scheme for the evaluation of students' work; and attempting to ensure an atmosphere conducive to teaching and learning. For the student, it entails responsible conduct respectful of the rights and needs of other students, fulfilling course requirements with work that represents commitment, and contributing to the collective enterprise of education.

The following guidelines concern the work that students undertake for courses in the Department of Film and Media, and the policies and practices of the Faculty of Arts and Science at ���Ҵ�ý.

Academic Integrity

�ϳܱ���’s University is dedicated to creating a scholarly community free to explore a range of ideas, build and advance knowledge, and share the ideas and knowledge that emerge from a range of intellectual pursuits.  �ϳܱ���’s students, faculty, administrators, and staff therefore all have responsibilities for supporting and upholding the fundamental values of academic integrity. Academic integrity is constituted by the five core fundamental values of honesty, trust, fairness, respect, and responsibility and by the quality of courage. These values and qualities are central to the building, nurturing, and sustaining of an academic community in which all members of the community will thrive. Adherence to the values expressed through academic integrity forms a foundation for the "freedom of inquiry and exchange of ideas" essential to the intellectual life of the University.

The following statements from “The Fundamental Values of Academic Integrity” (2nd edition), developed by the International Center for Academic Integrity (ICAI), contextualize these values and qualities:

1. Honesty     Academic communities of integrity advance the quest for truth and knowledge through intellectual and personal honesty in learning, teaching, research, and service.
2. Trust     Academic communities of integrity both foster and rely upon climates of mutual trust. Climates of trust encourage and support the free exchange of ideas which in turn allows scholarly inquiry to reach its fullest potential.
3. Fairness     Academic communities of integrity establish clear and transparent expectations, standards, and practices to support fairness in the interactions of students, faculty, and administrators.
4. Respect     Academic communities of integrity value the interactive, cooperative, participatory nature of learning. They honor, value, and consider diverse opinions and ideas.
5. Responsibility     Academic communities of integrity rest upon foundations of personal accountability coupled with the willingness of individuals and groups to lead by example, uphold mutually agreed-upon standards and take action when they encounter wrongdoing.
6. Courage     To develop and sustain communities of integrity, it takes more than simply believing in the fundamental values. Translating the values from talking points into action -- standing up for them in the face of pressure and adversity — requires determination, commitment, and courage.

Students are responsible for familiarizing themselves with and adhering to the Senate regulations concerning academic integrity, along with Faculty or School specific information. Departures from academic integrity include but are not limited to, plagiarism, use of unauthorized materials, facilitation, forgery, and falsification. Actions that contravene the regulation on academic integrity carry sanctions that can range from a warning to loss of grades on an assignment to failure of a course, to requirement to withdraw from the university.

�ϳܱ���’s (SASS) offers a self-directed, online academic integrity module that we encourage all students to take which will help with:

• Understanding the nature of the academic integrity departure
• Understanding the expectations of and role of sources in scholarly writing
• Integrating sources into your writing (paraphrasing, quoting, summarizing)
• Understanding when and how to cite your sources
• Managing your time effectively to avoid the need for shortcuts
• Taking effective notes to ensure the accuracy of source material and correct attribution

Policy on the Use of ‘Artificial Intelligence’:

Students must submit their own work and cite the work that is not theirs.  Generative AI writing tools such as ChatGPT are only permissible when explicitly noted in the assignment instructions. In these cases, be sure to cite the material that they generate. Any other use constitutes a Departure from Academic Integrity.

Overview

Since the release of ChatGPT in 2022, a range of algorithmic systems marketed as ‘artificial intelligence’ (AI) that are capable of generating and manipulating media such as text have become widely available. The Department of Political Studies’ policy on the use of these and similar tools in activities related to the Department, its courses, and its other work is as follows:

1. This policy is an elaboration of the University’s regulations on AI and academic integrity (see “Unauthorized content generation” under “What is a departure from Academic Integrity?”, “Guidance and Updates Regarding the Continued Approach to Generative Artificial Intelligence Tools in Education”, and “Guidelines for AI use in Graduate Research”). Adhering to the University’s academic integrity standard is a matter of ethics, professionalism, and respect.
2. Tools described as ‘AI’ may be used in a specific course for purposes explicitly described by the course instructor in the syllabus or other written communication. In this case some task delegation is not a violation of academic integrity, in the same way that sharing tasks in instructor-approved group work is not. If the use of AI is not explicitly permitted, it is prohibited by default.
3. Delegating a task that a student is supposed to perform themselves to another person, service, or tool is a violation of academic integrity known as ‘contract cheating’.
4. Suspected contract cheating will be subject to a formal investigation by the instructor/supervisor or a representative. If the investigation determines that there was a departure from academic integrity, a report will be filed with the Faculty of Arts and Science (FAS) Academic Integrity office, and the instructor/supervisor and/or the FAS will administer remedies or penalties/sanctions, which could include the requirement to withdraw from the university (see “Academic Integrity Procedures”).
5. Large language models (LLMs) such as ChatGPT and Gemini are inherently unreliable, frequently provide incorrect information, and are non-deterministic, meaning that the same prompt will generate different outputs each time the system is used (for more information,  please see the “What is ‘artificial intelligence’?” section below). Accordingly, even when their use is permitted for the purpose of generating or rephrasing text, these systems are not an appropriate substitute for sources that can be assessed by an instructor.
6. Research conducted using algorithmic systems may violate the University’s regulations regarding research integrity (a subcategory of academic integrity; see “Integrity in Research”) in a variety of ways, such as if you do not know the character or provenance of the system’s training data, whether the data was legally obtained, or how the data was modelled.
7. Ignorance is not a defence. Members of the Department who are unsure that they understand this policy or ‘artificial intelligence’ more generally are encouraged to read the rest of this page, which explains and contextualizes the policy, and to contact Dr. Stephen Larin if they have any further questions.

What is ‘artificial intelligence’?

By Stephen Larin

Artificial intelligence (AI) is a marketing term for a wide range of different computer-based algorithmic systems that has no single, unambiguous definition, but generally refers to ‘apparently intelligent action performed by a machine’. An ‘algorithm’ is a step-by-step procedure for accomplishing some purpose (performing a calculation, solving a problem, pattern-matching, etc.), and there is no inherent relationship between algorithms and computers; recipes are algorithms, for example. All computer programs/apps are based on many different algorithms, and there is no clear line between ‘just a program’ and ‘artificial intelligence’.

Conceptually, there are three broad categories of AI: ‘artificial narrow intelligence’ (ANI), ‘artificial general intelligence’ (AGI), and ‘artificial super intelligence’ (ASI). ANI is a task-specific algorithmic system that is capable of some autonomous intelligent action; AGI and ASI are speculative computational systems that are imagined to be capable of autonomous intelligent action across a range of domains and are either similar or superior to humans, respectively. ANI is the only type of artificial intelligence that actually exists; the other two are speculative fiction which do not, and may never exist. The strong influence of science fiction on popular perceptions of AI often leads to serious misunderstandings, however, so many researchers prefer terms such as ‘algorithmic system’.

ANI can be divided into two main types: symbolic ‘good old-fashioned AI’ and machine learning.

Symbolic AI was the dominant approach from the 1950s–80s and is ‘symbolic’ in the sense that it is based on human-readable symbolic programming language, similar to the symbols used in mathematics and formal logic. Its actions are determined by pre-programmed instructions that specify the range of options for a particular task and the best course of action.

Machine learning is the type of AI that has driven the surge of interest and development since the early 2010s. Unlike symbolic AI, a machine learning system is capable of doing things that its programmers did not foresee and program. It is designed with a core set of rules to follow, but ‘learns’ what to do within those parameters by being ‘trained’ on a data set, and in some cases also through operation. There are several different approaches to machine learning, but the most influential is ‘deep learning neural networks’. Don’t take the name too seriously—it’s aspirational, in the sense that it is supposed to model how brains work, but neural networks are not ‘artificial brains’ in any meaningful sense. Deep learning neural networks are very good at pattern-matching, and their performance has significantly improved since about 2012, but most people were unaware of these advances until ChatGPT was released in late 2022.

Large language models

ChatGPT and most of the other systems that are marketed as AI are ‘large language models’ (LLMs), which are a particular type of deep learning neural network system. They are called large language models because they ‘model’ the pattern of linguistic relationships in large datasets of text.

For example, ChatGPT is based on terabytes of text from the Internet, ranging from books to Reddit posts. When OpenAI was developing ChatGPT, they used a algorithmic system that conducted a statistical analysis of the patterns of relationships between the different parts of that text dataset, the end product of which is ChatGPT’s ‘language model’ (the representation of the patterns of association found in the training data).

The purpose of an LLM is to generate plausible, clear, and grammatically-correct prose that is a linguistic match for its input. That’s it. It is crucial to recognize that no algorithmic system has the capacity for understanding, and when ChatGPT appears to be ‘answering your question’ (for example), it does not understand either your question or the answer, but is instead just generating the statistically best match between your text and the patterns in its model (with some deliberate randomization added in to help avoid repetitiveness and mimic creativity).

This often happens to provide the right answer, especially when the input matches with something that is well-represented and uncontested in the training data, but the truth or falsity of the text that the system generates is irrelevant, and impossible for it to assess. Large language models, like all deep learning neural networks, are pattern-matching machines and “”, as Princeton computer science professor Arvind Narayanan puts it, using philosopher Harry Frankfurt’s term for speech that is intended to persuade without regard for the truth.

Here’s a brief conceptual summary (read the arrows as ‘is a type of’):

ChatGPT → large language model → deep learning neural network → machine learning → artificial narrow intelligence → algorithmic system

AI and academic integrity

The core principle of academic integrity is that all work that you submit for evaluation must be yours alone, because the university accreditation system is based on students demonstrating that they actually have the skills that their grades and degrees certify. Delegating a task that you were supposed to perform to an algorithm is no different than delegating it to another person, so delegating a task that you were supposed to perform to an algorithmic system violates academic integrity.

The ‘that you were supposed to perform’ part is key. In group work, for example, whatever you submit is meant to be the product of a collaborative effort. Similarly, some instructors may not only permit the use of algorithmic systems in their course, but even encourage or require it for an assignment. Some instructors permit some algorithmic systems and prohibit others, based on the tasks that they perform.

For example, some instructors recommend that their students use reference management software such as EndNote or Zotero. These are algorithmic systems that automate most of the citation process: if you need to cite something while you’re writing, you call up the reference manager, choose the source you want to cite, and the manager will automatically insert the reference you need on that page and add it to your bibliography, all formatted according to whatever citation style you are using. Automation is usually appropriate for tasks that are not integral to learning and for which ‘’ is helpful. This is the same reason that calculators aren’t prohibited in most Political Studies courses, but might be in those where the student is meant to be learning how to do some types of calculations on their own, if only so that you will genuinely understand what a calculator is doing when it does that type of calculation for you (which is very important in some professions).

On the other hand, algorithmic systems that generate or paraphrase/rewrite text are prohibited in most Political Studies courses. That is usually because writing is integral to both learning and evaluation in political science, and delegating that task undermines these things.

For example, when writing a paper, we often don’t really know what we think about something until we’ve typed out a few sentences, read them aloud, and rewritten them many times. Writing is a kind of self-dialogue that allows us to work out complex ideas and analyses because it, too, is a kind of cognitive offloading that facilitates the development and application of many core skills, including precise conceptualization, logical organization, and clear communication.

Given all of this, it should be obvious why the unauthorized use of an algorithmic system both violates academic integrity and doesn’t make any sense. It violates academic integrity because you are pretending to have done something that you didn’t do. It doesn’t make sense because it undermines the pedagogical purpose of the assignment. If you don’t even try to do your own work, you will never be able to do it. It’s like training for a marathon by driving the route in a car.

Ask for help when you need it

If you are struggling with your work, contact your instructors and ask to meet with them during office hours.  also offers a variety of services and opportunities, including one-on-one consultation, to help students improve their study and writing skills. William Zinsser’s book  (Harper Collins, 2006) is also highly recommended.

The politics of artificial intelligence

If you would like to learn more about the political and broader social implications of artificial intelligence, the documentary  (2020) is a good place to start. Kate Crawford’s book  (Yale, 2021) is currently the best overview of the politics of artificial intelligence, and Sasha Luccioni et al.’s “” is a good introduction to that under-studied subject. Students are also encouraged to take “POLS 478: Politics of Artificial Intelligence”.

Grading

In May of 2009, �ϳܱ���’s Senate approved the implementation of a new grading scheme, based on letter grades and a numerical grade point average (GPA).  Senate has defined the correspondence of percentage marks, letter grades, and grade points. The Department of Film and Media has developed the following rubrics as a framework for the assessment of student work.

A+ (90-100) 4.3 grade points

This mark indicates exceptional performance in both form and content. In addition to having mastered the content of the topic, the student has demonstrated the ability to apply the course material in new and creative ways and/or has shown an understanding of its wider context and significance. The paper is free of grammatical and formatting problems.

A (85-89) 4.0

This mark range recognizes performance demonstrating thorough knowledge of concepts and techniques and showing a high degree of skill and originality in satisfying the requirements of an assignment or course. The student’s work shows intellectual and creative initiative. The paper is free of grammatical and formatting problems.

A- (80-84) 3.7

This mark range indicates that the student has mastered the content of the course, a comprehensive understanding of concepts and techniques, and an ability to extend their application. The paper has a few modest grammatical or formatting errors.

B+ (77-79) 3.3

This mark range indicates that the student has assimilated essential concepts and techniques and shown skill in using them to satisfy the requirements of an assignment or course. The paper has several grammatical or formatting problems.

B  (73-76 ) 3.0

This mark range indicates broad awareness and competent use of concepts and techniques, in satisfying the requirements of an assignment or course. The paper has several grammatical or formatting problems, which are substantial enough to inhibit comprehension.

B- (70-72) 2.7

This mark indicates knowledge of the course material and comprehension of its essential concepts. The paper has numerous grammatical or formatting problems, which inhibit comprehension.

C+ (67-69) 2.3

This mark range indicates familiarity with concepts and techniques together with some ability to use them to satisfy the requirements of an assignment or course. The paper has several grammatical or formatting problems, which seriously inhibit comprehension.

C (63-66) 2.0

This mark range indicates a basic grasp of the essential concepts and techniques of a course. The paper has numerous grammatical or formatting problems, which inhibit comprehension to the extent that it makes reading difficult.

C- (60-62) 1.7

This mark range indicates limited acquaintance with the concepts and techniques of a course. The paper has numerous grammatical or formatting problems, which inhibit comprehension to the extent that it makes reading difficult.

D+ (57-59) 1.3

D (53-56) 1.0

D- (50-52) 0.7

These mark ranges indicate marginal performance. The student has minimally fulfilled the requirements for the assignment or course. The paper has numerous, severe grammatical or formatting problems, which greatly inhibit comprehension to the extent that it makes reading difficult. 

F 0-49 /0

This mark indicates that the student has failed to meet the minimum requirements of the assignment or course and has not demonstrated an adequate grasp of the material.

Grading Miscellaneous

A few important items to keep in mind about the new grading scheme, and general benchmarks:

Failing grades will be tabulated as part of the transcript.
A minimum GPA of 2.7 in Film & Media courses is required in order to advance into 4th year, and to graduate with Honors.
A minimum GPA of 2.7 in Film and Media courses will have to be maintained in order to advance from Second year standing to Third year.

Here are a few benchmarks associated with GPA:

3.9 -- Dean’s Honors list with distinction
3.5 -- Dean’s Honors list
2.8 -- graduate with Honors in Film and Media
1.6 -- graduate with a general degree (below 1.6 – probation).

Essay Writing Guidelines

The expression of ideas and communication of research form major parts of academic exercises; they are taken into account in the evaluation of a student's work and progress. Essays with numerous or serious errors in grammar, mechanics, and spelling may be returned for revision. For a recommended guide to the standards of academic writing and basic writing skills please refer to the syllabus of FILM 110 or FILM 206. We expect students to write essays in the MLA format, common in the humanities. 

Writing Standards for Filmmakers, Artists and ���Ҵ�ý

In today’s highly competitive professional environments, writing skills are essential. Any work done in academic and quasi-academic environments requires comprehensive writing skills. The same standards apply to film- and media-makers, artists and producers. Whether the artist/filmmaker is applying for residencies, or grants, or providing a written introduction or statement to a prospective employer/funder, writing is often the initial if not primary means of self-presentation. In a competitive world, if the person doing the initial sorting of applications spots poor writing, that application will be the first to be placed on the rejection pile. Writing skills are acquired through a thoughtful training process whereby one fixes mistakes or bad habits as they are identified.  The Film and Media Department is committed to students upholding and enhancing these standards in all written work.

General writing standards

• Each essay will be computer-printed (if a hard copy is required), double-spaced, titled, paginated, stapled, and carefully proofread.  
• Fonts should be set at 12-point in a readable, serif typeface (e.g., Times New Roman).
• Papers are due on the designated date.
• Proofread your work after you have printed out a final draft.  Papers that show a multitude of easily corrected errors may be returned ungraded and marked late.
• Footnote and formatting standard:   MLA (Modern Languages Association)

(see following guidelines and sources.)

General writing guidelines

• An introductory paragraph establishes a thesis or outlines the arguments, which will be concise and clear. 
• The paper should show thoughtful organization and arguments should be clearly articulated and presented in manageable units.
• Be careful to distinguish between arguments (which are backed up with the reasons one is making a claim) and assertions (which are unsubstantiated points of view).
• Please avoid unnecessary filler, such as unnecessary plot description.
• Carefully review your paper for errors and syntactical awkwardness; the writing should be clear and effective, without being wordy.
• Please format the paper correctly.

Note that a paper with a multitude of structural and grammatical problems will not be awarded a grade higher than a “B”. 

The Arts & Science Calenda includes regulations on Academic Integrity. You should familiarize yourself with the policy in order to avoid dishonesty or plagiarism. See also suggestions from the Department of Film and Media on Avoiding Plagiarism.

How to cite media in essays

Films with English title:

Citizen Kane (Orson Welles, USA, RKO, 1941)

Films with Foreign Language title:

Two or Three Things I Know About Her (Deux ou Trois choses que je sais d'elle, Jean-Luc Godard, France, Anouchka Films, 1967)

Artist and Activist Videos and Digital Media:

Moscow Does Not Believe in Queers (John Greyson, Canada, 1986)

Television Episodes:

“M�������������,”&�Բ�����;Breaking Bad (Michelle McLaren, USA, Sony Pictures Television, 2012)

Works cited - General guidelines

The alphabetical list of works cited that appears at the end of your paper contains more information about all of the sources you've cited, allowing readers to refer to them, as needed. The main characteristics are:

• The list of Works Cited must be on a new page at the end of your text
• Entries are arranged alphabetically by the author's last name or by the title if there is no author
• Titles of books are italicized and titles of articles are placed in quotation marks. All important words should be capitalized
• Entries are double-spaced (for the purposes of this page, single-spacing is used)
• For online sources, date of access is an optional element. However, it can be helpful to include this information, especially if the source you are using does not have a date of publication

Book w/ One Author:

Mumford, Lewis. The Culture of Cities. Harcourt, 1938.

Book w/ Two Authors:

Ormerod, Neil, and Christiaan Jacobs-Vandegeer. Foundational Theology. Fortress Press, 2015.

Book w/ Three or More Authors:

Francis, R. Douglas, et al. Destinies: Canadian History since Confederation. Harcourt, 2000.

Anthology or Compilation:

Abate, Corinne S., editor. Privacy, Domesticity, and Women in Early Modern England. Ashgate, 2003.

Work in an Anthology or an Essay in a Book:

Naremore, James. "Hitchcock at the Margins of Noir." Alfred Hitchcock: Centenary Essays, edited by Richard Allen and S. Ishii-Gonzalès, BFI, 1999, pp. 263-77.

Books by a Corporate Author:

Organisation for Economic Co-operation and Development. Action against Climate Change: The Kyoto Protocol and Beyond. OECD, 1999.

Article in a Journal:

Ferrer, Ada. "Cuba 1898: Rethinking Race, Nation, and Empire." Radical History Review, vol. 73, Winter 1999, pp. 22-49.

Article in a Newspaper or a Magazine:

Semenak, Susan. "Feeling Right at Home: Government Residence Eschews Traditional Rules." Montreal Gazette, 28 Dec. 1995, A4.

Webpage:

"Joyce Wieland." Celebrating Women's Achievements: Women Artists in Canada, 2000,  Accessed 29 Mar. 2004.

Late Submission Policy

A penalty of 3% per day (including weekends) will be applied to all late submissions, to a maximum of 10 days late.  Submissions made on the 10^th day or later will not be accepted and a mark of zero will be entered.

Appeals to wave the above penalties must be made directly to the instructor, and will only be awarded in cases of where documentation supports the claim of an unexpected interruption of studies. 

Attendance

Students are expected to attend all regularly scheduled classes and screenings for courses in which they are enrolled. An instructor may make attendance part of the grading scheme for a course.

Incomplete Grades

Incompletes (IN) are not automatically granted to students who have not submitted all required work in a course. A student may request that the instructor submit an incomplete grade to be adjusted once all course requirements are fulfilled. Requests for incompletes should be made on forms available in the departmental office and from the instructor. The instructor will provide the student a written indication of a date by which work must be submitted. A maximum of 120 days is granted to complete the work, after which an IN will automatically become a failing grade (F).

For further details including Academic Regulations, Conflict of Interest, and the ���Ҵ�ý Code of Conduct, see the Art and Science Calendar. 

---