Bibliographic record
Abstract
In this paper, we view creativity through the lens of innovation, a concept familiar to archaeologists across a range of contexts and theoretical perspectives. Most attempts to understand ancient innovation thus far, we argue, have been limited by their lack of capacity to cope with the multiple scales of innovation: Those that track widespread changes, like the beginnings of metallurgy, fail to account for the changes experiences by individual craftspeople; those that do justice to the details of the micro-scale, with ‘thick’ description, cannot well explain the regional adoption of new practices. Here we develop an intermediary position, at the meso-scale, which we hope can serve to integrate these different scales. It is based on the notions that all innovation entails learning (and hence cognitive transformations) and that learning is very often supported at this meso-scale, through ‘communities of practice’. Drawing on the ethno-archaeological literature in particular, we emphasise how learning is a process of embodied cognition. Our archaeological case study is then drawn from the Bronze Age east Mediterranean, where a striking innovation in pottery making — the use of rotative kinetic energy via the potter’s wheel — sees a very uneven uptake from region to region over the course of many centuries. We propose certain differences in community organization from one region to another that might account for such variation in the adoption of an innovation, with the island of Crete in particular seeing a much more stable trajectory of adoption than many of its neighbouring areas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".