Visualizing the Power and Privilege of Failure in Higher Education
Bibliographic record
Abstract
Learning from failure is a core component to education, however it is not often deliberately taught in university courses. In addition, while the rhetoric around taking risks, embracing failure, and bouncing back is pervasive in higher education, the corresponding structural supports are lacking. The purpose of the current work is to explore ways we can visualize and illustrate the power and privilege involved with embracing and learning from failure in the context of higher education. We offer three approaches to visualizing the same set of research data exploring student and instructor experiences of failure. The first figure is structured using a Venn diagram, the second uses a mobius strip, and the third draws on both puzzle imagery and the structure of a kernmantle rope to offer a more complex rendition of power and privilege in higher education. These illustrations are intended to serve as introductory guides to this topic. This work emphasizes that power is diffuse and mutable, and we underscore the critical importance of recognizing that each person will experience power and privilege differently in different circumstances. This exploration of illustrative concepts is a place to start theorizing about how students and instructors experience, resist, or wield power as they navigate academic institutions and engage with failure. We note that each instance of struggle, failure, or recovery exhibits specific configurations of power as multiple vectors contribute more or less strongly to the situation. The exact topography of power will change as different people, areas of the institution, or social policies and values enter the equation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".