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Record W4392079594 · doi:10.1515/9781787443921

Gained Ground

2018· book· en· W4392079594 on OpenAlexaboutno aff

Bibliographic record

VenueBoydell and Brewer eBooks · 2018
Typebook
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyGeography

Abstract

fetched live from OpenAlex

Compares the cultural productions of Canada and the US - literature, but also film, opera, and even theme parks - providing a reassessment of Canadian Studies within a comparative framework. Since the elections of Donald Trump and Justin Trudeau, unprecedented international attention is being drawn to the differences between the United States and Canada. This timely volume takes a close comparative look at the national imaginaries of the two countries. In its analyses of the two countries' cultural productions - literature, but also film, opera, and even theme parks - it follows the approach of Comparative North American Studies, which has been significantly advanced by Reingard M. Nischik's work over recent decades. Featuring such illustrious contributors as Linda Hutcheon, Sherrill Grace, and Aritha van Herk, the volume considers the works of writers such as MargaretAtwood, whose concern with both countries' identities is well known, but also offers surprising new insights, for example by comparing writing by Edgar Allan Poe with Canadian Yann Martel's novel Life of Pi and Nobel Prize-winning author Alice Munro's work with that of the American graphic novelist Alison Bechdel. Contributors: Margaret Atwood, Shuli Barzilai, Julia Breitbach, Jutta Ernst, Florian Freitag, Marlene Goldman, Sherrill Grace, Michael and Linda Hutcheon, Bettina Mack, Silvia Mergenthal, Claire Omhovère, Katja Sarkowsky, Aritha van Herk. Eva Gruber is Assistant Professor of American Literature at the University of Konstanz. Caroline Rosenthal is Professor of American Literature at the University of Jena.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.197
Teacher spread0.181 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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