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
* I welcome comments, corrections, and recommendations for improving this and future analyses of this topic. I thank Thomas Crowley for research assistance and a host of other professors at the MBA schools listed herein who responded to my requests for syllabi and for checking the information on the spreadsheets. I especially acknowledge help and insights from Laurie Hodrick, Ivo Welch, and Susan Chaplinsky. The reader should understand that this analysis attempts to capture a snapshot of a moving target. Courses and requirements are continually changing, so that even if the results are or were correct recently as of fall 2001, they will certainly change as professors teaching those courses change and as the MBA programs make incremental changes and improvements. Even though I have attempted to verify all information received from the target schools, there are almost certainly some errors in this analysis that I will gladly correct in future versions if I am alerted to them. I regret that data from Indiana University were not easily incorporated into this analysis. Future versions will be republished on my website and the FEN website.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it