Sampled to Death? The Rise and Fall of Probability Sampling in Archaeology
Bibliographic record
Abstract
After a heyday in the 1970s and 1980s, probability sampling became much less visible in archaeological literature as it came under assault from the post-processual critique and the widespread adoption of “full-coverage survey.” After 1990, published discussion of probability sampling rarely strayed from sample-size issues in analyses of artifacts along with plant and animal remains, and most textbooks and archaeological training limited sampling to regional survey and did little to equip new generations of archaeologists with this critical aspect of research design. A review of the last 20 years of archaeological literature indicates a need for deeper and broader archaeological training in sampling; more precise usage of terms such as “sample”; use of randomization as a control in experimental design; and more attention to cluster sampling, stratified sampling, and nonspatial sampling in both training and research.
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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 | 0.293 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.100 |
| Scholarly communication | 0.010 | 0.038 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".