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Lookup NU author(s): Enrico Croce, Dr Francesco CarrerORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2025. Inductive predictive modelling is a controversial tool in archaeology. Visibility, taphonomy and research history can affect the statistical reliability of an archaeological dataset to be used as a training sample for a predictive model. To overcome these biases, an ethnoarchaeological approach has been proposed. This methodology has been developed and tested on a pastoral context in the Eastern Italian Alps. The present research proposes an application to a different, more composite landscape in the Central Italian Alps, testing the reliability of the model in relation to a heterogeneous and diachronic dataset, collected through extensive fieldwork. The results show that the model has an excellent degree of accuracy in predicting past structures with a similar purpose of use as the training sample. In addition, we show that its discriminative power can be greatly improved by the use of contemporary environmental predictors. However, the use of variables non-quantifiable for the past is an issue for the full applicability of this type of model to archaeological datasets. The results also show that this methodology, regardless of predictive results, can give us a good insight into the relationship between humans and their environment. The field application of the methodology has led us to understand that ethnoarchaeology can already be considered a reliable approach to address various methodological concerns of archaeological predictive modelling, but the primary purpose of such models should be seen more as explanatory rather than predictive.
Author(s): Croce E, Carrer F, Angelucci DE
Publication type: Article
Publication status: Published
Journal: Journal of Archaeological Method and Theory
Year: 2025
Volume: 32
Issue: 3
Online publication date: 08/05/2025
Acceptance date: 01/05/2025
Date deposited: 27/05/2025
ISSN (print): 1072-5369
ISSN (electronic): 1573-7764
Publisher: Springer Nature
URL: https://doi.org/10.1007/s10816-025-09712-w
DOI: 10.1007/s10816-025-09712-w
Data Access Statement: The code and data generated by the development of the inductive predictive model are freely available online: https://doi.org/https://doi.org/10.5281/zenodo.14245171.
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