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Record W2259753140 · doi:10.1177/1532708615615610

Enacting Civility Through Comic Opera; or, Nation, Race, and Ned McGowan’s War

2015· article· en· W2259753140 on OpenAlexaff
Heather Davis-Fisch

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsCivilityComicsIdeologyOperaSpanish Civil WarWhite (mutation)HistoryColonialismAestheticsRacismLiteratureSociologyPoliticsLawGender studiesArtArt historyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

Popular historians have frequently described the strange events now known as Ned McGowan’s War in theatrical terms, specifically as comic opera. By taking up June Schlueter’s suggestion that “virtually all” definitions of genre “have rested their case on the ending,” this essay considers how the “generic end” of this comic opera “pointedly involves itself in ideological concerns” by examining the social world that emerges via the climax and denouement of the “War.” Considering the fears of American annexation and occupation that motivated the colonial response to the “War,” its “happy” ending reveals a highly unstable relationship between civil action and national identity at this particular moment. By considering what—and specifically who—fails to be incorporated into the world that emerges at the end of the comic opera, this essay argues that emergent notions of “white civility” in the colony relied on the drawing of and policing of strategic boundaries, in this case along not national but racial lines. The “War” was comprised of social performances which reveal not only the extent to which colonial British Columbia was a “performing society” but also that the boundaries of civil society were negotiated through theatricalized social performances.

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)0.003
metaresearch head score (Gemma)0.103
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.103
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.004
Scholarly communication0.0000.001
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 head0.312
GPT teacher head0.469
Teacher spread0.157 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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