MétaCan
Menu
Back to cohort
Record W2079794782 · doi:10.5539/ells.v3n4p7

A Post-Colonial Study of Fenimore Cooper’s The Last of the Mohican: Relativity, Racism, Hybridity, and American Dream

2013· article· en· W2079794782 on OpenAlexvenueno aff
Hajiali Sepahvand

Bibliographic record

VenueEnglish Language and Literature Studies · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiterary Theory and Cultural Hermeneutics
Canadian institutionsnot available
Fundersnot available
KeywordsHybridityDreamIndependence (probability theory)ColonialismMythologyIdentity (music)RacismHistoryAmerican literatureDecolonizationHEROGender studiesLiteratureSociologyAestheticsArtPolitical scienceLawClassicsPsychologyArchaeologyPolitics

Abstract

fetched live from OpenAlex

The study tries to explore some post-colonial themes in J. Fenimore Cooper’s Last of Mohican. For doing so, it traces the elements including: relativity, racism, hybridity, and American Dream by which Cooper abrogates legitimized superiority of Europe and inferiority of American, although he has appropriated European English and changed it for American local needs. In fact, he reveals the post-colonial condition of America to define Americanism, American identity, hero, and myth in his novel. In actuality, the real concern of a colonized person, like Cooper, is to object to colonizers; therefore, we investigate his novel which was shaped in the transitional period of colonization to their independence as the reflection of internal voice of a nation against invaders. The article came to the conclusion that Cooper, by his novel, serves to introduce American autonomous novel and literature which is the greatest manifestation of culture. That is, he tries to announce American cultural independence through literature, namely decolonization.

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)0.003
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0280.016
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0020.004
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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language and Literature StudiesSame topicLiterary Theory and Cultural HermeneuticsFrench-language works237,207