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Between Raid and Rebellion: The Irish in Buffalo and Toronto, 1867–1916

2016· article· en· W4254177876 on OpenAlexaboutno aff
Cian T. McMahon

Bibliographic record

VenueJournal of American Ethnic History · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHistory of the bookIconIrishCitationPublishingHistoryLibrary scienceArtArt historyComputer scienceLiteraturePhilosophy

Abstract

fetched live from OpenAlex

Book Review| January 01 2016 Between Raid and Rebellion: The Irish in Buffalo and Toronto, 1867–1916 Between Raid and Rebellion: The Irish in Buffalo and Toronto, 1867–1916. By William Jenkins. Montreal: McGill-Queen’s University Press, 2013. xx + 511 pp. CAD$110 (cloth). Cian T. McMahon Cian T. McMahon Search for other works by this author on: This Site Google Journal of American Ethnic History (2016) 35 (2): 124–125. https://doi.org/10.5406/jamerethnhist.35.2.0124 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Cian T. McMahon; Between Raid and Rebellion: The Irish in Buffalo and Toronto, 1867–1916. Journal of American Ethnic History 1 January 2016; 35 (2): 124–125. doi: https://doi.org/10.5406/jamerethnhist.35.2.0124 Download citation file: 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 Scholarly Publishing CollectiveUniversity of Illinois PressJournal of American Ethnic History Search Advanced Search The text of this article is only available as a PDF. Copyright 2016 by the Board of Trustees of the University of Illinois2016 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 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.487
Threshold uncertainty score0.980

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.005

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 head0.029
GPT teacher head0.283
Teacher spread0.255 · 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 designQualitative
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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