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Comprehensive Academic Integrity: Academic Ethics in a Postplagiarism Age
(2023-10-04) Eaton, Sarah Elaine
Abstract
If you think academic integrity is only about student conduct, you may be living in the past. In this opening keynote, Dr. Sarah Elaine Eaton, provides insights from the latest research around the world that shows how academic and research integrity include, and extend student conduct. She’ll bring insights from the Handbook of Academic Integrity (2nd ed.), which shows how our understandings of academic integrity in school provides a foundation for ethical decision making beyond the classroom. Dr. Eaton also talk about how artificial intelligence is challenging historical notions of plagiarism and sets the stage for important conversations that will happen throughout the conference.
Keywords: postplagiarism, academic integrity, plagiarism, artificial intelligence, neurotechnology, brain computer interface (BCI), ethics, education, higher education, student conduct, futurism, future, history. 35 Figures; 36 References
2025 Addendum
This keynote address marked the first time I presented on the concept of postplagiarism. It was also the first time I began speaking about the ethical impact of neurotechnology and brain computer interfaced (BCIs) in education. This talk is based on my 2023 editorial for the International Journal for Educational Integrity, published the same month.
The slides, transcript, and notes are publicly available as an open access resource, under a Creative Commons By-Non-Commercial-No Derivatives license.
An Attention-Based Deep Learning Approach for Forecasting Electricity Prices in Real-Time Electricity Markets
(2025-01-15) do Carmo Junior, Jose Eustaquio; Zareipour, Hamidreza; de Souza, Roberto Medeiros; Papalexiou, Simon Michael
Real-time electricity markets are characterized by irregular and sudden price swings, leading to high price volatility and significant uncertainties for market participants. Accurate and informative electricity price forecasts are essential to reduce these uncertainties and enable more effective decision-making in energy generation, consumption, strategy planning, and risk management. This thesis presents a new forecasting framework for electricity prices in real-time markets, leveraging the capabilities of deep learning and advanced feature engineering. The proposed methodology integrates the Temporal Fusion Transformer (TFT), a deep learning model applied to time series forecasting, with dynamic clustering techniques to enhance forecasting accuracy. This is done by combining Hierarchical Density-Based Spatial Clustering of Applications with Noise and Dynamic Time Warping to cluster generators based on attributes such as geographical location, fuel type, installed capacity, and generation patterns. These clusters provide new covariates for forecasting models, enabling the method to adapt to the unique characteristics of any electricity market. The effectiveness of the proposed framework is demonstrated through a case study of the Ontario electricity market, where the methodology outperforms the forecasts of the system operator in terms of average error, accuracy, precision, and recall across a six-hour forecast horizon. This study underlines the framework's versatility and offers valuable insights into electricity price behavior, aiding market participants in mitigating risks and optimizing energy strategies.
Whatls Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias
(2025-01-10) Mohammadshahi, Aida; Ioannou, Yani Andrew; Far, Behrouz H.; Bento, Mariana Pinheiro
Knowledge Distillation is a commonly used Deep Neural Network (DNN) compression method, which often maintains overall generalization performance. However, we show that even for balanced image classification datasets, such as CIFAR-100, Tiny ImageNet and ImageNet, as many as 41% of the classes are statistically significantly affected by distillation when comparing class-wise accuracy (i.e. class bias) between a teacher/distilled student or distilled student/non-distilled student model. Changes in class bias are not necessarily an undesirable outcome when considered outside of the context of a model’s usage. Using two common fairness metrics, Demographic Parity Difference (DPD) and Equalized Odds Difference (EOD) on models trained with the CelebA, Trifeature, and HateXplain datasets, our results suggest that increasing the distillation temperature improves the distilled student model’s fairness, and the distilled student fairness can even surpass the fairness of the teacher model at high temperatures. This study highlights the uneven effects of distillation on certain classes and its potentially significant role in fairness, emphasizing that caution is warranted when using distilled models for sensitive application domains.
Mythologies of Outer Space
(University of Calgary Press, 2025-01-15) Humble, Noreen; Ellis, Jim
Every culture and society has read stories in the night sky. From the careful attention of astronomers across all times and all parts of the world to the search for alien life, the stories found in the shapes of constellations to the expansive imaginings of science fiction, there has always been life up there, at the very least, for our imaginations.
Mythologies of Outer Space brings together academics and artists to explore diverse imaginings of outer space. It examines questions that, in a world where outer space is increasingly accessible, are no longer only science fiction. Is outer space terra nullius, open for settlement? What if there is life beyond earth? Will we repeat the mistakes of the colonial age on other planets? Should parts of outer space be protected, like nature reserves? What about resource extraction? Do celestial bodies, like the moon, have rights?
Astronaut Robert Thirsk, Mi’kmaw astronomer Hilding Neilson, digital humanities scholar Chris Pak, and outer space archaeologist Alice Gorman, among others, are joined by artists including David Hoffos and Dianne Bos, literary scholars, art critics, scientists, and a poet to explore how humanity thinks about outer space in this joyful, curious book.
Ill-fated Mission: Canada’s Franklin Expedition and its Role in Arctic Securitization
(2025-02) Clifton, Robert; Huebert, Rob; Sayers, Anthony Michael; Rice, Roberta L.; Huebert, Rob
In 1845, Sir John Franklin and his crew went missing while in search of a Northwest Passage in the Canadian Arctic. The shipwrecks were discovered in 2014 and 2016 (respectively) with the first, The HMS Erebus, discovered under the administration of Canadian Prime Minister Stephen Harper (2006-2015). Employing discourse analysis of qualitative research interviews and primary documents, and the process tracing method, this thesis investigates the role the Franklin Expedition searches and discoveries played in the Harper government’s Canadian Arctic securitizing move. This thesis tests the original Copenhagen, and Second Generation, Securitization theories for applicability to the Harper-era Franklin searches and communications in the context of Canada’s Arctic security policy. It finds mild support for the relevance of Second Generation theories over the original Copenhagen theory from which later theories of securitization evolved. Given analysis of theoretical underpinnings and after deep qualitative analysis, this study finds that the Franklin searches and discovery did not play a central role in Prime Minister Harper’s prior securitization of the Canadian arctic.