Bibliographic record
Abstract
Abstract Long before the emergence of Roman historical writing, the indigenous societies of Iron Age Italy were actively engaged in the transmission and use of the past. This book draws innovatively from anthropological and archaeological theory to provide a first account of this early historical interest. Key is an expansion from history narrowly defined to historical culture: the forms of written, narrative history familiar to us today are by no means the only manner in which societies articulate past and present. A new and more expansive understanding of what history is helps us recognize pervasive historical interests across a wider range of media. From the Early Iron Age to the third century BCE, Italian communities used a rich variety of burial practices, objects, calendars, and images to record and transmit their history. This book brings all this material together under the rubric of historical culture. The assembled evidence shows how Italy’s historical culture transformed over time as a reflection of broader changes in Italian social structure. The historical culture of Early Iron Age communities in Italy was transformed by the emergence and development of urban society before ultimately being absorbed by Roman imperial power. As the case of Iron Age Italy shows, different societies had different customs, and this applies to the ways in which communities engaged with their past. The account provides readers with an accessible presentation of several recent and important archaeological discoveries from Pre-Roman Italy.
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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 | 0.001 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".