Bibliographic record
Abstract
Introduction It is often appropriate to model the spatial organisation of human activity in terms of point locations and the relationships between them; for example the movement of goods between settlements or the intervisibility between forts. This chapter discusses the various network analysis tools that can be used to study such relationships. It also discusses techniques for predicting the likely path of an unknown route between point locations, as well as the flow of water and watersheds. Given that the bulk of archaeological data is ultimately point based it is surprising that network analysis has not featured more prominently in the archaeological application of GIS. Of course, what is a point at one scale of analysis may be a region at another, and it is thus important to recognise that the applicability of network analysis is determined by the way in which the problem is framed rather than the geographical extent of a particular study. A few published archaeological network analyses have investigated subjects ranging in scale from the colonisation of new territory (Allen 1990; Zubrow 1990) and the location of ‘centres’ (Bell and Church 1985; Mackie 2001) to the connectivity of rooms in individual buildings (Foster 1989). There is no reason why this range could not be extended to even smaller extents: to, for example, investigate patterns of refitting among lithic artefacts in a single stratigraphic unit.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".