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Record W4411167860 · doi:10.51270/47.2.155

Institutions and Regional Integration on the Maritime Peninsula: Why Natural History Societies Still Matter

2023· article· en· W4411167860 on OpenAlexvenueno aff
Gabriel Hrynick

Bibliographic record

VenueCanadian Journal of Archaeology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPeninsulaNatural historyNatural (archaeology)HistoryPolitical scienceGeographyArchaeologyEcologyBiology

Abstract

fetched live from OpenAlex

The Maritime Peninsula, the eastern homeland of the Wabanaki, forms a defensible archaeological region, but one divided by an international border. Research from either side of the border remains poorly integrated. In this paper, I consider the history of the institutional-scale research in the region—that is, the organizations supporting, publishing, and regulating archaeological research. Archaeology in the State of Maine developed an outward orientation early on, with research largely sponsored by out-of-state institutions. In contrast, work in the Maritime provinces (Maritimes) was dominated by local natural history societies. A relative dearth of research on both sides of the border for much of the twentieth century served to secure these trends before they were calcified legislatively at the provincial level in the Maritimes and in connection with federal legislation in Maine. As a result, archaeology in the Maritimes is marked in large part by a focus on objects and inventories of objects, a generalist approach that blurs historical and precontact archaeology, and an iterative approach to defining archaeological significance. In contrast, work in Maine tends to emphasize survey and the definition of sites and is more clearly problem oriented with conservative criteria for historical significance. As a result, attempts at regional integration may need to be aimed at some of these scales.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.073
GPT teacher head0.319
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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