Oral Traditions and the Archaeological Record of a Wabanaki Maritime Society
Bibliographic record
Abstract
This thesis examines prehistoric watercraft documented in the region now inhabited by the Wabanaki, an indigenous maritime society living in New England and the Canadian Maritimes, from archaeological and oral traditions perspectives.Archaeological research has been slow to accept oral traditions as valid, independent sources of evidence.The paucity of prehistoric watercraft and associated tool kits in this study requires exploring Wabanaki prehistory through alternative sources.I gathered oral traditions from a St. Francis Abenaki elder, a Wabanaki oral historian and storyteller, and a traditional Wabanaki canoe artist to tie together historical and archaeological data using maritime socio-cultural relations in the form of oral traditions and histories.Watercraft remains have not been preserved in the archaeological record, requiring an alternative approach, defined within the parameters of this thesis as an oral traditions methodology, to study the maritime technological adaptations of the Wabanaki.This methodology may serve as a template for similar archaeological studies, historic and prehistoric, within societies that value the accurate transmission of oral traditions in the absence, as well as presence, of material remains.In particular, I aim to facilitate a better understanding of Wabanaki technology within the maritime environment of New England and the Canadian Maritimes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".