Survey Planning, Allocation, Costing and Evaluation (SPACE) Project: Developing a Tool to Help Archaeologists Conduct More Effective Surveys
Bibliographic record
Abstract
Designing an effective archaeological survey can be complicated and confidence that it was effective requires post-survey evaluation. The goal of SPACE is to develop software to facilitate survey designers’ decisions and partially automate tools that depend on mathematical models so that archaeologists can conduct surveys that accomplish their goals and evaluate their results more easily. We aim for SPACE to be a modular and accessible web-based platform for survey planning and quality assurance, with a “front end” that has a non-threatening, question-and-answer format. Its several interacting modules will ultimately include ones for evaluating visibility, estimating sweep widths and coverage, costing, determining sample sizes, transect and test-pit intervals, allocating effort optimally for stratified samples and predictive surveys, and quality assurance. In this paper, we focus on the module for estimating fieldwalkers’ sweep widths on the basis of “calibrations” on fields seeded with artifacts, while also reviewing the overall structure of the project. Sweep widths are critical for estimating coverage, evaluating survey effectiveness and quality, and planning transect intervals.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".