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Record W2330148981 · doi:10.3167/hrrh.2014.400301

Rescuing Early America from Nationalist Narratives: An Intra-Imperial Approach to Colonial Canada and Louisiana

2014· article· en· W2330148981 on OpenAlexvenueaboutno aff
Daniel H. Usner

Bibliographic record

VenueHistorical Reflections/Réflexions Historiques · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismNationalismNarrativeHistoryCharacter (mathematics)EthnologyGenealogyPolitical scienceGeographyLawPoliticsArchaeologyLiteratureArt

Abstract

fetched live from OpenAlex

The effort to compare and connect different French colonies in North America encounters some treacherous roadblocks, including the powerful impact of Canadian and United States nation building on the treatment of French colonial regions and the widely divergent approaches taken by scholars of New France and French Louisiana. This essay attempts to explain why these obstacles appeared in the first place and to suggest how they might be overcome in the future. At a time when historians of early America are vigorously seeking new analytical frameworks and meaningful historical narratives, intraimperial research on complex relationships and comparative issues in French North America constitutes an essential area of study. Whether examining the role of Canadian families in the founding of Louisiana, the influence of Acadian settlers on south Louisiana culture, or the character of Indian relations in French colonies—among other issues—a shared history of early Canada and Louisiana will significantly improve our understanding of North American peoples and places.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.134
Threshold uncertainty score0.975

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0490.032
Scholarly communication0.0150.004
Open science0.0020.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2014
Admission routes2
Has abstractyes

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