Bibliographic record
Abstract
Foreword: Michael Jackson. Editor's Introduction: Steven D. Hales (Bloomsburg University). Part I: The Art of the Beer:. 1. Thirst for Authenticity: An Aesthetics of the Brewer's Art: Dale Jacquette (Pennsylvania State University). 2. The Beer Matrix: Reality vs Facsimile in Brewing: Garrett Oliver (Brooklyn Brewery). 3. The Truth About Beer: Michael P. Lynch (University of Connecticut). 4. Good Beer, or How to Properly Dispute Taste: Peter Machamer (University of Pittsburgh). 5. Quality, Schmality: Talking Naturally about the Aesthetics of Beer or, Why is American Beer So Lousy?: Martin Stack (Rockhurst University) and George Gale (University of Missouri). 6. Extreme Brewing in America: Sam Calagione (Dogfish Head Craft Brewing). Part II: The Ethics of Beer: Pleasures, Freedom, and Character:. 7. Mill v. Miller, or Higher and Lower Pleasures: Steven D. Hales (Bloomsburg University). 8. Beer and Autonomy: Alan McLeod (Senior Legal Counsel for the City of Kingston, Ontario). 9. Another Pitcher? On Beer, Friendship, and Character: Jason Kawall (Colgate University). Part III: The Metaphysics and Epistemology of Beer:. 10. Beer and Gnosis: The Mead of Inspiration: Theodore Schick (Muhlenberg College). 11. The Unreasonable Effectiveness of Beer: Neil A. Manson (University of Mississippi). 12. What's a Beer Style?: Matt Dunn (University of Indiana at Bloomington). Part IV: Beer in the History of Philosophy:. 13. Drink on, the Jolly Prelate Cries: David Hilbert (University of Illinois at Chicago). 14. Beer Goggles and Transcendental Idealism: Steven M. Bayne (Fairfield University). 15. Beyond Grolsch and Orval: Beer, Intoxication, and Power in Nietzsche's Thought: Rex Welshon (University of Colorado at Colorado Springs). Index
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.038 | 0.018 |
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".