Deepening relational capacity to confront the polycrisis in higher education and beyond
Bibliographic record
Abstract
This article examines the multifaceted challenges confronting Canadian higher education, situating these against a backdrop of volatility, uncertainty, complexity, and ambiguity (VUCA) and the poly-/meta-/perma-crisis. We examine 10 specific challenges, including uncertain finances; affordability crisis; complexities of equity, diversity, inclusion, decolonization, and Indigenization; intergenerational dissonance; public (ir)relevance; ecological destabilization; ambivalent AI; mental health epidemic; hyper-polarization; and lack of capacity for coordination. While these challenges threaten the stability of our institutions, they also offer opportunities for higher education to catalyze institutional, societal, and systemic transformations that prioritize intergenerational and interspecies responsibility. We suggest moving in this direction will require staff and faculty to deepen our relational capacities and offer a case study to illustrate this possibility.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.079 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".