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Inoculating against an infodemic: microlearning interventions to address CoV misinformation
— 2021-03-04 Publications are forthcoming The dataset was created as part of the CIHR-funded project, 'Inoculating against an infodemic: Microlearning interventions to address CoV misinformation'. This research examines digital misinformation flows pertaining to the 2020 COVID-19 pandemic for the purpose of developing educational interventions to reduce the spread of online misinformation. The dataset contains transcripts of 45 one-to-one, semi-structured interviews that were conducted in June/July 2020. These interviews were used to gather data about how Canadians engaged with COVID-19 information online. Two different sets of interview questions were used: 18 of the transcripts follow protocol A, and 27 follow protocol B. Both protocols asked the same initial questions about COVID-19 information habits. Protocol A then asked questions about interviewee-provided media samples, while protocol B asked questions about interviewer-provided media samples. Due to the sensitivity of the data this data set will only be available to vetted researchers upon request. To request access to the data, contact the lead author, Jaigris Hodson.
Royal Roads University Dataverse Translation missing: fr.blacklight.search.logo
Inoculating against an infodemic: microlearning interventions to address CoV misinformation
Borealis
Hodson, Jaigris — 2021-03-04 The dataset was created as part of the CIHR-funded project, 'Inoculating against an infodemic: Microlearning interventions to address CoV misinformation'. This research examines digital misinformation flows pertaining to the 2020 COVID-19 pandemic for the purpose of developing educational interventions to reduce the spread of online misinformation. The dataset contains transcripts of 45 one-to-one, semi-structured interviews that were conducted in June/July 2020. These interviews were used to gather data about how Canadians engaged with COVID-19 information online. Two different sets of interview questions were used: 18 of the transcripts follow protocol A, and 27 follow protocol B. Both protocols asked the same initial questions about COVID-19 information habits. Protocol A then asked questions about interviewee-provided media samples, while protocol B asked questions about interviewer-provided media samples. Due to the sensitivity of the data this data set will only be available to vetted researchers upon request. To request access to the data, contact the lead author, Jaigris Hodson.
Royal Roads University Dataverse Translation missing: fr.blacklight.search.logo
The Morality and Tax Avoidance: A Sentiment and Position Taking Analysis
Borealis
Lokanan, Mark — 2023-06-26 This dataset consists of textual transcript analysis from the Parliamentary Commission on Banking Standards on tax avoidance in the U.K. The data is used to examines the moral and legal underpinnings of corporate tax avoidance. Cast in terms of a totemic symbol that brand tax avoidance as within the purview of the law, the paper invokes the attributional frames of the new sociology of morality to examine the position of both the moral advocates and the amoral critics of aggressive tax avoidance.
Royal Roads University Dataverse Translation missing: fr.blacklight.search.logo
Determinants of Investment Fraud
Borealis
Lokanan, Mark — 2022-09-28 This research aims to examine investment fraud cases in Canada by coding cases retrieved from the Investment Industry Regulatory Organization of Canada's website. The dataset consists of features related to enforcement, offenders, and victims and consists of numeric, float, and categorical variables. This research will contribute to understanding investment fraud in Canada and provide information that can be used to prevent and detect investment fraud.
Royal Roads University Dataverse Translation missing: fr.blacklight.search.logo
Fraud Exploitation
Borealis
Lokanan, Mark — 2022-10-05 The purpose of this dataset is to build machine learning classifiers to predict exploitation of securities fraud victims in Canada. The dataset consists of numeric, float, and categorical variables. The dataset also consist of features related to victims’ demographics, financial profile, and investments. This work was done with funding from a SSHRC Insight Development Grant

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