On the Causes of the Greatness and Magnificence of Cities
Bibliographic record
Abstract
extol the virtues of a great literary text, a fact Weinberg and Matter easily acknowledge and are justified in labelling the "author's intention to emulate Plato […] an obvious failure" (42).However, for those of us who study dialogue, even a "bad" one is irresistible, as it often reveals much about how the genre was used and perceived during its most prolific time period.The praise of its writing is correctly deferred to those instances where Alfieri recounts conditions and historical events to which be bore witness either as a participant or an observer.They cite his "best and most credible pages" as those that outline "Milan after the death of Gian Galeazzo, Genoa at the time of Gabriele's execution, and Caffa at the beginning of its political and economic decline" (43).The Ogdoas' contribution to political history is undoubtedly its greatest strength according to our editors who cite Alfieri's list of consuls in the city of Caffa between 1409 and 1421 as "important details that fill a gap in the history of the colony" (43).In bringing Alfieri's work to light, Weinberg and Matter have provided a valuable text of the Renaissance, which further reveals the thoughts and conditions of a people and a time whose fascination, continues unabated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".