Bibliographic record
Abstract
Abstract Rome reached its peakancientpopulation when, in the second centuryCE, it supported as many as one million inhabitants – a number twice that of even the largest rival city in the Mediterranean. This statistic is impressive in itself, but it is also important as a striking illustration of the unique nature of the city within the Roman Empire, where no other city even came close in total population. Rome's privileged position was due primarily to one factor: it was home to the emperor, who controlled activities in the city either directly or through the appointed prefects and other officials who by the Antonine period had supplanted the traditional senatorial magistrates responsible for the maintenance of the urban fabric and its inhabitants (Hammond 1959: 444–6). The emperor's influence on the population and the physical structure of the city can hardly be overestimated – nor can the extent to which the monuments of the capital are able in turn to illuminate the character of the ruler: the intense familial piety of Antoninus Pius, the restraint of Marcus Aurelius, the insanity of Commodus and, most interestingly of all, the mysterious character of Hadrian.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".