Bibliographic record
Abstract
Introduction Spatial analysis lies at the core of GIS and builds on a long history of quantitative methods in archaeology. Many of the foundations of spatial analysis were established by quantitative geographers in the 1950s and 1960s, and adopted and modified by archaeologists in the 1970s and 1980s. For a variety of reasons, spatial analysis fell out of fashion both in archaeology and in the other social sciences. In part this was because of the perceived overgeneralisation of certain types of mathematical models, but also because of a shift towards more contextually orientated and relativist studies of human behaviour. Recently, however, there has been a renewed interest in the techniques of spatial analysis for understanding the spatial organisation of human behaviour that takes on board these criticisms. In the last decade there have been several advances within the social sciences, particularly geography and economics, in their ability to reveal and interpret complex patterns of human behaviour at a variety of scales, from the local to the general, using spatial statistics. Archaeology has participated somewhat less in these recent developments, although there is a growing literature that demonstrates a renewed interest in the application of these techniques to the study of past human behaviour. In this chapter we review some historically important methods (e.g. linear regression, spatial autocorrelation, cluster analysis) and also highlight more recent advances in the application of spatial analysis to archaeology (e.g. Ripley's K , kernel density estimates, linear logistic regression).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.126 | 0.053 |
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".