Bibliographic record
Abstract
Currently, of Abenaki groups of Indigenous peoples, only the Maliseet, Passamaquoddy, and Penobscot have successfully been organized as federally recognized tribes in the United States, but historically, there have been at least a dozen Abenaki tribal groups residing in what is now New England and New Brunswick.Most of them intermingled with one another during the past colonial period.Historical accounts and manuscripts often use various Abenaki nomenclatures created linguistically by outsiders.Abenaki tribal identity can be retraced through these records by comparing place names, demography, lifestyles, as well as the geographic areas inhabited and trading relations affirmed during conferences between colonial authorities and Abenaki chiefs in 1713, 1717, 1721, and 1727.However, outsiders' observations and attempts at naming these groups confused and misled the recognition of the identities and correct names of the Abenaki groups.Accordingly, this thesis focuses on elucidating the political and geographical context for the Kwupahag of the Eastern Abenakis along the St. John River in New Brunswick and the Muanbissek of the Western Abenakis along the Merrimack River in New Hampshire.Using a digital humanities approach to examine the correlations among these Abenaki groups, I analyzed a series of graphs created with Gephi, the open software that can map a kind of network to specify the characteristic relationships between each of Abenaki groups and settlers to a researcher by querying the graphs.The graphs produced can support efforts to maintain and recover Indigenous rights, memory, cultures, identity, and sovereignty, by visually representing historical relationships between Abenaki peoples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".