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Record W7099124562

METANARRATIVES AND TEXTUAL IRONIES IN ROBERT KROETSCH'S GONE INDIAN UDC 820(71).09-31

2001· article· en· W7099124562 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMythologyHEROTricksterNarrativeIronyRidiculousIdentity (music)CovertContent (measure theory)
DOInot available

Abstract

fetched live from OpenAlex

Abstract. The narrative structure of Robert Kroetsch's novel Gone Indian is multi-layered and abounds in the use of mythological fragments with special reference to the myths and stories of the Canadian past and present. Robert Kroetsch's use of myth in his novels, as well as in the novel Gone Indian, is exposed to parody and irony for he believes that familiar myths and stories must be "deconstructed", "uninvented " and "unnamed " (all his favored terms) as fictions. The surface story of the novel Gone Indian is concerned with the issues of identity of the main hero Jeremy Sadness whose life story, recorded in fragments on the tape re-corder, is manipulated and interpreted by another narrative voice in the novel, Jeremy's professor and supervisor, Mark Madham, who plays the role of a parodic reader-surrogate and writer-surrogate. What is of interest here is not the overt structure and content of the narrative, but the covert structures, hidden in "the space between ' the text and the actual reader. Three of the most significant texts beneath the surface story of Gone Indian are: Frederick Jackson Turner's notion of the American frontier; the story of "Gray Owl", a type of an ironic trickster figure in the text; and Jack Shadbolt's mural at the Edmonton

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.094
GPT teacher head0.396
Teacher spread0.302 · 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
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
Published2001
Admission routes1
Has abstractyes

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