RADIOCARBON DATING OF TECHNOLOGICAL TRANSITIONS: THE LATE HOLOCENE SHIFT FROM ATLATL TO BOW IN NORTHWESTERN SUBARCTIC CANADA
Bibliographic record
Abstract
Precolumbian archaeologists traditionally focus on periods of stability rather than change when constructing regional cultural chronologies. However, the advent of large databases of radiocarbon dates and the proliferation of open-source software environments such as R now allow archaeologists to understand technological transitions with greater chronological precision than has been historically possible. In this study, we employ Monte Carlo procedures, Bayes’ Theorem, the R package Bchron, and IntCal13 to address three chronological topics. We calculate the minimum number of dates required on atlatl and bow technologies to robustly date this late Holocene transition in Subarctic northwestern Canada, analyze previously published dates on organic projectile diagnostics to determine whether bows and atlatls overlapped for an observable amount of time, and estimate the years of calendric time that they overlapped. Results indicate that minimum sample sizes of 29 atlatl and 19 bow dates are required to characterize this particular transition in our study area. Actual radiocarbon dates show that bow and atlatl technologies temporally co-occurred within this region for 174 ± 135 (1σ) actual calendar years. Quantitative analyses such as these open the door to testing hypotheses that explain why and how technological transitions occur within and between (pre)historic groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".