Kirp, David L. 2003. Shakespeare, Einstein, and the Bottom Line: The Marketing ofHigher Education
Bibliographic record
Abstract
As the twenty-first century begins to bloom, perhaps no entity will meta- morphose as much as higher education-and economic factors are clearly a major force driving these changes.The Higher Education Act (set for renewal this year) allocated over $52 billion for 2002.The higher education complex, as a whole, expended well over $200 billion-that's roughly the GDP of the Governor of California Arnold Schwarzenegger's native Austria.What makes this topic of further import is it affects every one of us.We are students, parents or children of students, educa- tors, taxpayers, and the recipients of university-led innovations.Still, it is sometimes difficult to weave academia into our day-to-day lives, but David L. Kirp comes to the rescue.His latest book, Shakespeare, Einstein, and the Bottom Line: The Marketing of Higher Education, hit the bookshelves in November, and it's a must read for anyone who cares about education (who doesn't fit into this category?).While it would be impossible for anyone to write a 300-page treatise exploring the entirety of higher education economics, Kirp does a remarkable job of synthesizing the information we ought to know.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".