Quantitative Data, Hypothesis Testing, and Archaeological Narratives: Was there really a Greek Dark Age?
Bibliographic record
Abstract
This paper assesses the increasingly common view that the Greek "Dark Age" is a scholarly construct by determining whether the rate of discovery of Early Iron Age sites has increased since the discovery of Lefkandi around 1980. I present a database of over 4,000 Late Bronze Age and Protogeometric sites discovered between 1900 and 2010. If Early Iron Age sites were being systematically ignored in the early 20th century, but treated fairly in the last few decades, we would expect the annual rate of Protogeometric site discovery to increase after 1980 in a way that diverges from the rate of discovery of Bronze Age sites. However, the data show that the rates of discovery for the two periods normalize very well, to roughly 1:1 over the whole data set. This data suggests that the poverty of the Early Iron Age cannot be explained as the result of scholars ignoring the evidence.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".