Bibliographic record
Abstract
Ethnoarchaeology … an excellent means of getting an exotic adventure holiday in a remote location … After figuring out what you think is going on with the use and discard of objects (you should never stay around long enough to master the language) you return to your desk and use these brief studies to make sweeping generalisations about what people in the past and in totally different environments must have done. ( Paul Bahn 1989: 52–3 ) As archaeologists began to do ethnography in the service of archaeology, they unaccountably adopted many ethnographic techniques of gathering data. ( Michael Schiffer 1978: 234 ) Experience has taught us that some consciousness-raising about the differences between archaeological and ethnoarchaeological fieldwork is necessary before young archaeologists are let loose to deal with live “subjects” in the field. This chapter does a little of that but is no substitute for a manual on research methods and the conduct of ethnographic and sociological fieldwork. Of these there are many (e.g., Bernard 1994; Babbie 1998; Berg 1998) to which we strongly recommend that all refer. A second purpose is to encourage critical reading of ethnoarchaeological studies. We are here concerned to establish standards rather than to criticize particular examples, and we will comment on method in discussion of the case studies treated in later chapters. What information about the production of an ethnoarchaeological work does the reader require to evaluate its conclusions?
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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 | 0.032 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".