Bibliographic record
Abstract
Achieve more while using finite resources.That is the challenge alluded to by William Bowen in the book Higher Education in the Digital Age.The book compiles Bowen's address at the 2012 Tanner Lecture on Human Values at Stanford University in California.The Tanner Lecture is hosted annually at different international universities (e.g., Princeton, Yale, Stanford, and the University of Utah in the United States as well as Cambridge and Oxford in the United Kingdom).The purpose of the Tanner Lecture is to advance scholarly and scientific learning with respect to human values, transcending national, cultural, and subject-specific boundaries.As such, the format of the book is unusual; it begins with the author's lecture and it includes the responses from the discussants who participated in the Stanford event.Bowen's credentials as an economist (specifically, an expert in the economics of higher education), administrator (the former president of Princeton University), and innovator (founding chairman of ITHAKA, a not-for-profit organization that supports the academic community through digital publishing) provide the foundation for the book and the lecture that preceded it.The book is brief and very readable for a layperson-I have no background in administration or economics.However, the brevity of the book leaves some noticeable gaps, and certain key issues are given insufficient detail.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".