Using Actor-Network Theory to Characterize the Production of Ancient Maya Caching Events at Cerro Maya (Cerros, Belize)

Inspired by actor-network theory, this research uses an operationalized archaeological actor-network approach to characterize and examine human-object relationships associated with ritual caching deposits (votive bundles of objects) at the site of Cerro Maya (formerly Cerros), Belize. Designed to be...

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Veröffentlicht in:Journal of archaeological method and theory 2021-12, Vol.28 (4), p.1027-1057
Hauptverfasser: Vadala, Jeffrey, Duffy, Lisa
Format: Artikel
Sprache:eng
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Zusammenfassung:Inspired by actor-network theory, this research uses an operationalized archaeological actor-network approach to characterize and examine human-object relationships associated with ritual caching deposits (votive bundles of objects) at the site of Cerro Maya (formerly Cerros), Belize. Designed to be broadly applicable for archaeological studies, our archaeological actor-network approach made it possible to inductively examine, characterize, and diachronically compare the complex arrays of human and nonhuman relationships. In contrast to previous studies that characterized caches mainly in symbolic terms, we treated caches as traces of the small-scale actor-networks that emerged during the production of ancient Maya caching events. More specifically, our actor-network methodology made it possible to characterize caches and caching events in terms of the relationships between materials, temporality, objects, places, and groups of people, their intentions, and actions. The inductive and diachronic focus of approach also allowed us to compare arrays of caching actor-networks over time while considering the social affect that caching events had on subsequent caching events and the site’s social development. This approach demonstrates that even simple artifact clusters can be viewed as proxies for highly complex networks of interlinked social relations that play roles in shaping important historical interactions and social orders over time.
ISSN:1072-5369
1573-7764
DOI:10.1007/s10816-020-09485-4