Bibliographic record
Abstract
On his recent retirement from the chair of classical archaeology in Cambridge University, Anthony Snodgrass reflected on the state of the subject, wondering whether a paradigm shift has occurred. Snodgrass assesses various matters, including, for our purposes, how archaeological approaches to ancient literary sources have changed. His comments deserve quotation in full: …Classical archaeology is often stigmatized, by its many critics, as being ‘text-driven’ … [in] that the subject takes its orientation from, and adapts its whole narrative to, the lead given by the literary sources. Thus the archaeology of Roman Britain has been built around Tacitus' narrative of conquest; the study of Greek art around the text of the Elder Pliny; the archaeology of fifth-century Athens around the narratives supplied by Herodotus, Thucydides and Xenophon; that of Republican Rome similarly around those of Livy and Diodorus; that of Sicily again around Thucydides; and most notoriously, that of Aegean prehistory and protohistory around Homer…. But there is a deeper level still. Traditional Classical archaeology is stated…to have directed its energies at those aspects of the ancient world on which the written sources, taken as a whole, throw light. Thus, on urban but not on rural life; on public and civic, but not on domestic activity; on periods seen as historically important, but not on the obscurer ones; on the permanent physical manifestations of religion, but not on the temporary ones – sacrifice, patterns of dedication, ritual meals, pilgrimage; on the artefacts interred in burials, but not on burial itself; on the historically prominent states – in Greece, Athens and Sparta – but not on what has recently been called ‘the Third Greece’…
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".