Bibliographic record
Abstract
Archaeology and Traditional Knowledge sometimes find themselves pitted against each other in the quest for legitimacy, for an audience. When discussing the origins of the Native populations of the Americas, some very serious emotions can be touched upon. You only have to revisit the whole question of the Kennewick Man controversy over these past few years to see how this "contest" came down, quite literally, to fighting over the bones of these ancient people. On the one hand, scientists had a wonderful opportunity of studying very scarce remains dating to the pre-8500 year ago period. Yet local Native Americans invoked NAGPRA (Native American Graves Protection and Repatriation Act) to claim the remains as their own and insist on their reburial and especially their withdrawal from scientific study. Judges and lawyers got into the fray and arguments were advanced in front of tribunals. Does this mean that archaeologists should shy away from discussing the issues, from laying out what evidence does exist to advance one theory over another? Absolutely not. In fact, the contrary is clearly indicated. We need to enter into a dialogue (well outside of the legal system, I would hasten to add) and we have to ensure that the public has access to as much information as possible in order for their opinions to be better informed because public opinion, whether we like it or not, influences the political and even the legal processes that can and sometimes do impinge upon archaeological inquiry.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.356 | 0.229 |
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".