When the University Becomes an Obstacle or <i>Re-Storying the University</i>
Bibliographic record
Abstract
There are several banners with messages created by the University of Toronto (UofT) placed alongside the major roads of its three campuses. In 2022, these messages included “Moving Toward Equity,” “Putting a Feminist Lens on Economic Recovery,” and “Obstacles are Motivations to Push Forward.” I remember seeing these banners and asking myself, “What happens when the university becomes the obstacle?” In this article, the author wants readers to think seriously about the university as an obstacle and what that means when thinking about another university now. One of these banners, Obstacles are Motivations to Push Forward, represents the university as a teleological space for forward movement. Like all other banners it is accompanied by the slogan Defy Gravity, part of the university’s fundraising campaign, that cites “the climate crisis,” “economic and social inequalities,” “systemic racism,” “injustices against Indigenous peoples,” and the COVID-19 pandemic, as reasons to “ rise and move forward together.” But what does it mean to move forward when the structures you are pushing against are built on neoliberal logics, corporatized institutional structures, and settler-colonial violence? If the university is a problem space that we inhabit, how can we ask a different set of questions with which to imagine another university? If the colonial-capitalist roots of the Canadian university foreclose other futures, is change even possible? Can the university be a locus for change if it is simultaneously an obstacle to change?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.027 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".