MEASURING CHRONOLOGICAL UNCERTAINTY IN INTENSIVE SURVEY FINDS: A CASE STUDY FROM ANTIKYTHERA, GREECE*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper considers how to make the most out of the rather imprecise chronological knowledge that we often have about the past. We focus here on the relative dating of artefacts during archaeological fieldwork, with particular emphasis on new ways to express and analyse chronological uncertainty. A probabilistic method for assigning artefacts to particular chronological periods is advocated and implemented for a large pottery data set from an intensive survey of the Greek island of Antikythera. We also highlight several statistical methods for exploring how uncertainty is shared amongst different periods in this data set and how these observed associations can prompt more sensitive interpretations of landscape‐scale patterns. The concluding discussion re‐emphasizes why these issues are relevant to wider methodological debates in archaeological field practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it