Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Kobayashi Maru is a training simulation that has its roots in the Star Trek series notable for its defining characteristic as a no-win scenario with no “correct” resolution and where the solution actually involves redefining the problem. Drawing upon these characteristics, we designed a board meeting simulation for an experiential course in nonprofit governance, which places students in a high-stakes decision-making situation closely modeled on real events. To do so, we uniquely integrated principles from acting literature with theory and research in training and development. The Kobayashi Maru Meeting is a simulation with high physical and psychological fidelity—that is, one that closely resembles the “look and feel” of real-world board governance. The topics are deliberately sensitive to personal, organizational, and societal values to create high engagement and deep learning and to highlight the importance of good governance for organizational leadership. Results from multisource, multimethod data suggest that the simulation enhanced students’ decision making, critical thinking, and communication skills, as well as their ability to deal with their own and others’ reactions in intense circumstances. Beyond board governance, the simulation creates an authentic learning experience that can be adapted to multiple learning contexts including leadership, ethics, decision making, and communication.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it