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Record W2343841428 · doi:10.1525/phr.2016.85.2.304

Review: French Canadians, Furs, and Indigenous Women in the Making of the Pacific Northwest by Jean Barman

2016· article· en· W2343841428 on OpenAlexaffabout
Carol Williams

Bibliographic record

VenuePacific Historical Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsIconIndigenousCitationHistoryDownloadArt historyLibrary scienceWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Book Review| May 01 2016 Review: French Canadians, Furs, and Indigenous Women in the Making of the Pacific Northwest by Jean Barman French Canadians, Furs, and Indigenous Women in the Making of the Pacific Northwest. By Jean Barman. (Vancouver, University of British Columbia Press, 2014. xiv + 458 pp. $43.95 paper) Carol Williams Carol Williams University of Lethbridge Search for other works by this author on: This Site PubMed Google Scholar Pacific Historical Review (2016) 85 (2): 304–306. https://doi.org/10.1525/phr.2016.85.2.304 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Carol Williams; Review: French Canadians, Furs, and Indigenous Women in the Making of the Pacific Northwest by Jean Barman. Pacific Historical Review 1 May 2016; 85 (2): 304–306. doi: https://doi.org/10.1525/phr.2016.85.2.304 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentPacific Historical Review Search This content is only available via PDF. © 2016 by the Pacific Coast Branch, American Historical Association2016 Article PDF first page preview Close Modal You do not currently have access to this content.

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)0.002
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.360
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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
GenreReview

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

Citations0
Published2016
Admission routes2
Has abstractyes

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