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A Time and a Place: The Geography of British, French, and Aboriginal Interactions in Early Nova Scotia, 1726–44

2015· article· en· W1086165513 on OpenAlexaboutno aff
Jeffers Lennox

Bibliographic record

VenueThe William and Mary Quarterly · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHuman settlementNova scotiaSovereigntyNegotiationGeographyPolitical scienceSettlement (finance)EthnologyPeriod (music)Economic historyEconomyHistoryArchaeologyLawPoliticsArt

Abstract

fetched live from OpenAlex

British Nova Scotia in the early eighteenth century had overlapping geographic identities. French officials and settlers called the region Acadia, while Native polities inhabited their homelands as they had for thousands of years. The Mi’kmaq lived in Mi’kma’ki, the Wulstukwiuk and Passamaquoddy were sustained by the Wulstuk River, and the Abenaki Dawnland stretched across what is now Maine. During a period of relative peace from 1726 to 1744, these Native and non-Native groups monitored settlements, movements, and borders to prevent any unacceptable displays of territorial authority. In so doing, they created shared spaces of interaction: economic (fishing and trading), diplomatic (negotiating and gift-giving), and religious (worshipping and communing). Sovereignty remained elusive and European settlements were pales, but exchanges took place at various seasonal or temporary sites that allowed various groups to maintain peace, air grievances, and balance territorial control. These sites were created, supported, and allowed to dissolve as necessary. British officials did not always employ these sites effectively, whereas the French benefited from a longer history of interaction with the Native groups, who remained the dominant force in the region. Imperial claims to sovereignty were tempered locally by officials who recognized that their limited authority was a boon to peace.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2015
Admission routes1
Has abstractyes

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