Addressing the Alien in the Room: Why Public Perception is Imperative to the Field of Archaeology
Bibliographic record
Abstract
Pseudoscience in archaeology, or pseudoarchaeology, are ideas formed by distrust, with minimal observable evidence that explain the human past. In a world of widespread, accessible misinformation, researchers often dismiss the ideas presented within pseudoscientific theory as laughable or irrelevant. On the contrary, many of these thoughts are supported by and for colonialist or racist agendas. With popular media throughout North America now supporting pseudoarchaeology, misinformation is beginning to take a hold on public perception of the field of archaeology. To explore this influence further, this paper summarizes the origins and thoughts presented within popular pseudoarchaeology, current public understanding of archaeology, and why this matters to archaeologists. This paper primarily considers how archaeology is portrayed in Canada and the United States, although I use additional international examples to underscore the importance of global public engagement and media influences within the field of archaeology. Stressing the lack of accurate representation of archaeology, especially regarding the representation of Indigenous peoples, provides an invitation to strive for public engagement and honest discourse about the field.
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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.110 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| 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; 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".