Bibliographic record
Abstract
This chapter invokes a range of individual agencies behind life on Antikythera over the full history of its documented exploitation by humans. In particular, we wish to understand — via recent history, ethnography and, where possible, archaeological data — the impact of certain kinds of people whose roles, we would argue, are more central on small islands than they would be otherwise. Such people can arguably be lumped under the three broad headings of the eccentric, the specialist and/or the displaced and include hunters, colonists, cash-croppers, monastics, refugees, pirates, exiles, soldiers, hermits, retirees, modern-day tourists, expatriates and indeed various kinds of academic researcher. This chapter continues to emphasise the variable strategies for human mobility and varying degrees of long-term investment that we raised in the preceding chapter, but also argues that such generic parameters are given their structure by some very specific kinds of human personality and immediate circumstance. PIRATES Of all of the aforementioned human activities, it is piracy, hunting and herding that have been some of the most persistent occupational attractors in An-tikythera's history. Antikythera offers an opportunity to reconsider existing models of Mediterranean piracy over the long term, and there are four aspects that we wish to emphasise here: (1) useful distinctions of scale and type in piratical activity; (2) the sociology of pinch-points; (3) the ideology of piracy; and (4) the material culture of pirates.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".