Bibliographic record
Abstract
He is an expert on ships and shipbuilding in the Middle Ages and Early Modern period and, therefore, an expert on the emerging trade routes and networks that brought about our current era of globalization.He's also an expert on beer as evinced by his publication of Beer in the Middle Ages and Renaissance (2007), which was rightly described by both Choice and the Medieval Review as "an important book."Students everywhere would agree.The book points to the evolution of the beverage in the Middle Ages and the societal, nutritional, and economic roles it played.As no small recognition of his achievements, he was made president of the Medieval Academy of America in 2013, guiding it and its renowned flagship publication, Speculum, into the brave new world that is medieval studies at present.Professor Unger was kind enough to join Chris Mielke for a wonderful interview that recorded some very interesting exchanges on the many topics of his expertise.Imagine, then, our horror when we discovered that the sound file of the recording was corrupted.The second part was marred by a horrible screeching sound -one which we assure you was unrelated to either the interviewer or interviewee.However, as you will see in this transcript, it really is a delightful example of a varied and insightful conversation, courtesy of a (very well) established academic.So, regardless of the sound quality problems that prevented an earlier airing of it in our Past Perfect! series, we have chosen to include this very special interview for our volume.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.022 |
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".