The past and future of Indigenous archaeology: Global challenges, North American perspectives, Australian prospects<sup>1</sup>
Bibliographic record
Abstract
Since childhood, I've been drawn to distant times and diverse cultures. This interest provided the impetus, and my parents the encouragement, to pursue a career as an archaeologist. It was a decision that I have never regretted, even though what anthropology is today, and how it is practiced, is very different from that which 1 was first exposed to just a few decades ago. As will become apparent, 1 view archaeology as an inseparable dimension of anthropology (and vice versa), reflecting the Boasian four-field model that is prevalent in North America. One mandate of anthropology, regardless of where it is practiced, is to document and interpret cultural diversity to obtain a deep understanding of what it is to be human-a slightly more sophisticated endeavor than my adolescent forays into National Geographic magazine. In this, we seek not only the Other, which has come to represent non-Western peoples whose lives and worldviews fall outside the realm of familiar experience, but also ourselves. As archaeologists, we are also required to seek representativeness, to ensure that our work encompasses, both methodologically and theoretically, the range of past human endeavor.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".