Saying what we mean, meaning what we say: Managing miscommunication in archaeological prospection
Bibliographic record
Abstract
Abstract In North America, archaeological prospection has recently undergone a surge in popularity, resulting in higher visibility for both scientific and fringe narratives. This has been partially due to increasingly sensationalized media articles that promote the use of technology to locate overgrown and subsurface features in the landscape. The heightened profile of the field and increasingly sensitive contexts in which it is applied (e.g., locating potential unmarked graves) has expanded the discipline beyond its usual settings where typical archaeological prospection rhetoric and narratives are applied. In this paper, we explore how the presentation of archaeological prospection can impact descendant communities and their burial and cultural spaces. We identify rhetoric, discourse and narrative as key considerations that have resulted in the twisting of interpretations to support fringe narratives. We present two case studies: (1) denialism surrounding unmarked graves at former Indian Residential Schools and (2) the reinterpretation of Indigenous spaces by Graham Hancock's Ancient Apocalypse. We draw upon these seemingly disparate examples as evidence that ambiguity in scholarly communication and ‘certainty’ in fringe communication can both be used to the detriment of Indigenous and other descendant communities in various ways that we term pseudoarchaeological colonialism. Finally, we recommend strategies on how to disseminate results in non‐harmful ways and confront the wrongful usage of archaeological prospection.
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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 | 0.059 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.008 |
| 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".