Patterns of Etruscan urbanism (dataset and R scripts)
Bibliographic record
Abstract
The present digital archive is the outcome of the paper: Stoddart, S., Palmisano, A.,Redhouse, D.I., Barker, G., di Paola, G., Motta, L., Rasmussen, T., Samuels, T., and Witcher, R., 2020. Patterns of Etruscan urbanism. Frontiers in Digital Humanities, 7 (1). The dataset included here provides a collection of archaeological sites dating from the ninth to the fifth century BC, uncovered by systematic field survey carried out in central Italy (Tuscany and Lazio). In addition, the digital archive related to this paper provides reproducible analyses in the form of six scripts written in R statistical computing language. The present repository contains also a R Markdwon tutorial to drive step by step any practitioner interested in running some analytical tools to assess regional centralisation and settlement hierarchies: site-size histograms, rank-size graphs, A-coefficient, and B-coefficient. List of versions: 2.0. 31 March 2020 — Includes a few minor error corrections (the files 'Tutorial.RMD' and 'Tutorial.html'). 1.0 31 March 2020 — First public release of the dataset on Zenodo.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.175 | 0.133 |
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".