Structural Pattern Mining in Inka Khipus: A KFG Re-Analysis
Organizations: Independent Researcher
Abstract
This paper reports a methodological pivot from the Open Khipu Repository to the Khipu Field Guide (KFG) database, prompted by stalled progress on the OKR, and a full re-analysis of the resulting 650-khipu sample. Three findings recur across every analysis attempted. First, khipu size confounds nearly every derived structural count, explaining 42-70% of its variance; all subsequent models control for it. Second, the two-letter name prefix (AS, UR, HP, QU, JC, MM, KH) identifies the original cataloger, not a site or period, which turns documentation completeness into a second confound that must be modeled explicitly. Controlling for both, khipus cataloged by Hugo Pereyra's Proyecto Quipu (HP) show significantly fewer Ascher sum-relation types than Gary Urton's (UR) khipus (p = 0.044), while the Ascher-cataloged (AS) khipus are too collinear with their own near-total lack of side/direction documentation to assess (VIF = 5.0). Third, generic structural clustering (UMAP+HDBSCAN) does not separate the pooled 650-khipu sample from two independent null models (permutation and Gaussian), corroborating an independent conclusion already reached by the KFG itself; the same test restricted to a single cataloger (UR) proved not reproducible across runtime environments and is reported here as a methodological caveat rather than evidence of structure. A targeted replication of the KFG's own S/Z-knot spatial-block heuristic for calendar/astronomical khipus recovers 8 of 10 published candidates and proposes 34 additional ones under the same criterion.
Figures & tables
| Prefix | Cataloger | (this sample) |
|---|---|---|
| UR | Gary Urton | 369 |
| AS | Marcia & Robert Ascher | 144 |
| HP | Hugo Pereyra Sánchez (Proyecto Quipu) | 56 |
| Other/mixed | (MM, KH; not documented in the source above) | 36 |
| JC | Jon Clindaniel | 23 |
| QU | Kylie Quave | 22 |
| Cataloger | Coefficient vs. UR | -value | VIF |
|---|---|---|---|
| HP | 0.044 | 1.1 | |
| QU | 0.093 | 1.1 | |
| MM | 0.192 | 1.1 | |
| KH | 0.594 | 1.0 | |
| AS | 0.769 | 5.0 | |
| JC | 0.647 | 1.1 |
Historical Document Analysis
Historical document analysis uses AI to transcribe, interpret, organize, and retrieve information from archival texts and images.
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