University Commons Divided: Exploring Debate & Dissent on Campus by Peter MacKinnon
Bibliographic record
Abstract
Reviewed by: University Commons Divided: Exploring Debate & Dissent on Campus by Peter MacKinnon Jessica E. Shiers-Hanley Peter MacKinnon. University Commons Divided: Exploring Debate & Dissent on Campus. Toronto, ON: University of Toronto Press, 2018. 168 pp. Paper: $16.22. ISBN 9781487522827 In recent years, it seems universities are making it into news headlines at an increasingly frequent rate, yet this is not due to a scientific breakthrough or a surge of funding. In a world where social justice and extreme political correctness are becoming more commonplace, universities are more in the spotlight now than ever before when it comes to their faculty conduct and university policies. In University Commons Divided: Exploring Debate & Dissent on Campus, Peter MacKinnon explores a number of recent controversies that have taken place in universities across Canada. Issues such as academic freedom, social justice and equity, and freedom of speech are explored using real world situations. MacKinnon examines the new challenges universities are facing in an era where open debate and disagreement are now under fire. In University Commons Divided: Exploring Debate & Dissent on Campus, MacKinnon sets to address the issue of the commons being divided by taking a deep dive and analysis into a range of issues that have arisen in Canadian universities. MacKinnon explores issues such as academic freedom through the board of governors at the University of British Columbia and Carleton University. Free speech is tested by examining student protestors at the University of Calgary, and freedom of religion is challenged through Trinity Western University. MacKinnon also explores issues facing students with a Facebook case at Dalhousie University. MacKinnon revisits each scenario covered from a different lens in order to give the reader a sense of all possible outcomes that could have occurred. University Commons Divided is organized into seven chapters, six of which each explore a different issue MacKinnon uses to demonstrate how he believes the university commons are divided. MacKinnon starts his book off at the University of British Columbia by examining the Jennifer Berdahl case. Berdahl had speculated on a blog post as to why a UBC president had resigned when no explanation had been provided. The scenario brought into question the academic freedom of Berdahl, and whether or not she had academic freedom, which typically only pertains to teaching and research, not personal blog posts. This is where we are introduced to a theme that will be common throughout this book, in which the lack of clear definition of academic freedom can cause many debates and issues in the commons. MacKinnon meticulously examines this particular situation [End Page E-7] in detail, and from multiple angles. From the get-go, the author shows that he intends to give his audience a comprehensive understanding of the different paths that can be taken for each issue that is addressed. Moving away from a faculty centered issue, MacKinnon shifts gears to an issue that impacted students at Dalhousie University in regards to a social media post in a private group that was labeled as "sexist, misogynistic, and homophobic" (pg. 21). The university ultimately took a restorative justice approach to handling the issue rather than a disciplinary one. MacKinnon discusses at length the approach Dalhousie took. He later commends the leadership at Dalhousie, stating on page 34: The first reaction of many in the midst of scandal or public controversy is a rush to get in front of the issues, to be on the right side of those issues (and to establish their side as right), and to pressure leaders to declare themselves immediately, and take decisive action quickly. Certainly, leadership must move with dispatch to ensure that supports are in place for victims, but more deliberate approach is called for in determining the appropriate response to the transgressions. MacKinnon later makes the point that this matter not only affected students, faculty and staff, but also the surrounding community. With social media commonplace, it begs the question whether or not university administration should get involved in social media posts and private groups created by its students. Where is the line drawn? This is an aspect that was left out of this chapter, but is an important angle to consider. The issue of social...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.061 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".