Trace, Revelation, and Interpretant in Archaeological Research: The Graffiti of Huaca Colorada, Peru
Bibliographic record
Abstract
Abstract Romantic notions that the advancement of archaeological knowledge depends on the thrill of unanticipated discoveries departs from the standard practice of interpreting data according to impartial research designs. However, the unexpected find commonly stymies the deductive testing of hypothesis, and the material traces (signs) upon which research relies often disrupt the course of archaeological investigations. The main objective of this article is to demonstrate that the distinct semiotic affordances of material remains can significantly affect archaeological interpretations. The undertheorized epistemological problems of revelation in archaeology are brought to bear through an examination of the spectral quality of graffiti etched onto the walls of the Moche ceremonial site of Huaca Colorada in northern Peru (CE 650–850). An interpretation of the graffiti in relationship to rituals of human sacrifice and architectural renovation demonstrates that the power of the monument was founded on its semiotic density; the complex layering of signs—that continually spawned new signs—created a place of limitless discovery and affect that profoundly shaped perceptions of thehuacafor both Moche visitors and later archaeologists alike. Ultimately, the graffiti provide a rare data set that permits a consideration of the effects of signs as “intepretants” in the tradition of Peirce.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".