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Record W2995685597 · doi:10.7202/1065390ar

Epic Triumph, Epic Embarrassment, or Both?

2019· article· en· W2995685597 on OpenAlexvenueaboutno aff
Timothy S. Forest

Bibliographic record

VenueOntario History · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsEmbarrassmentVictoryPrideEPICPeninsulaHistorySpanish Civil WarLawPolitical sciencePoliticsArchaeologyLiteratureArt

Abstract

fetched live from OpenAlex

Widely divergent national and local historiographies about the War of 1812 have been created on both sides of the Canadian/American border. This has special significance for the Niagara region, where the two belligerents directly confronted and still confront each other. On the Canadian side of the Niagara River, the Niagara Peninsula has been transformed into a center of regional and national pride. Military leaders, troops and battles are celebrated and events commemorated in a grandiose and triumphant manner. Across the river, Western New York has largely downplayed or outright ignored its role in the very same conflict, to fit in with both the national and local amnesia surrounding the war and the region’s self-conceptualization as a “good neighbour”. This article focuses on public commemorations on both sides of the Niagara to uncover the historical, geographic, economic and demographic explanations as to why the war has been transformed into an epic victory for some, a defeat for others, and an embarrassment best ignored for many.

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)0.002
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.142
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0160.033
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.022
GPT teacher head0.225
Teacher spread0.204 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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