Judging the institution: Simulated jurisprudence and the governance of professional futures
Bibliographic record
Abstract
How might universities redesign their governance structures to be fit for professional futures? This paper explores the growing misalignment between academic appeals frameworks and the regulatory demands of professional programs, focusing on teacher education in Ontario. Using an AI-enabled simulation inspired by The Case of the Speluncean Explorers, the study stages a deliberation among fictional justices embodying diverse legal and ethical logics, including feminist jurisprudence, Indigenous law, regulatory oversight, and institutional proceduralism. The simulation functions as a foresight tool, surfacing the frictions that arise when public trust, ethical accountability, and professional suitability are filtered through academic governance structures not built for such complexity. Drawing on policy sociology and anticipatory governance theory, the paper proposes a Policy Futures Agenda with five interdependent design principles: anchoring governance in public trust, recognizing regulatory specificity, embracing epistemic pluralism, leveraging dialogic foresight tools, and treating professional faculties as sites of policy innovation. These principles offer a roadmap for institutions seeking to move beyond compliance and toward a more integrated, anticipatory governance model for professionally regulated education.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".