Methodological framework demonstrates structural trajectory modeling for low-frequency archaeological records, suggesting ordered behavioral reconstruction without causal bias.
Low‑frequency archaeological behaviours—engraving, ochre processing, notational marking, bone‑tool modification—are typically represented as isolated anomalies rather than components of structured behavioural trajectories. Their manifestations are sparse, unevenly distributed, and frequently contested, rendering traditional typological, statistical, and cultural‑transmission approaches insufficient for reconstructing ordered behavioural sequences [1–4]. This paper defines the Chronotope Method as a formal representational framework for organising such behaviours into coherent spatio‑temporal structures under conditions of evidential sparsity and taphonomic heterogeneity. A chronotope is a structured representational object composed of nodes, edges, directional flows, coherence gradients, and graded evidential states. It distinguishes structural alignment from causal interpretation, ensuring that directional flows are treated as ordered similarity rather than cultural lineage. The method integrates a geometry‑based taxonomic protocol [2,5], a two‑axis coherence model bounded by taphonomic discipline [6], and a generalised Model A/B sensitivity procedure for evaluating structural invariants under alternative evidential configurations. A diagnostic protocol formalises the treatment of contested evidence through the unresolved‑but‑modelled category [7], enabling structural evaluation without evidential inflation. The chronotope method generalises across archaeological domains while remaining epistemically conservative, domain‑neutral, and non‑committal with respect to symbolism, cognition, and transmission. An appendix provides non‑rigorous illustrative fits in other regions and behavioural domains. The chronotope method therefore establishes a testable, conservative, and structurally grounded analytical instrument for reconstructing behavioural trajectories across deep time.
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Thomas Filsecker (2026) studied this question.
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