Bibliographic record
Abstract
Of course, I hope this is not the last word since the point of this exercise has been to stimulate discussion about what are very important issues for higher education. I was delighted when JET decided to publish as a forum piece with several reactions followed by these final thoughts from me. I asked several academic administrators from Penn State to provide an administrator's point of view. I felt that, since might be perceived as a reaction to Penn State, it was appropriate to give them a chance to respond. They declined, gracefully, because it might look contrived. I turned to my friend Dave Forsythe from the University of Nebraska to provide the administrator's vantage. I knew Professor Forsythe could not deny his faculty roots and would have interesting ideas. I asked an old friend and colleague, Ted McDorman, from the Law Faculty at the University of Victoria, to provide a Canadian perspective on Lucy. This gets to the heart of a major issue, that is, is Lucy a uniquely American phenomenon? The answer provided by Professor McDorman and Ms Lindgren, yes and no, is the only right
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 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.042 | 0.021 |
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".