MétaCan
Menu
Back to cohort
Record W4327699814 · doi:10.5430/wjel.v13n5p110

Post-Independence Themes in Arun Joshi’s Novel: The Apprentice

2023· article· en· W4327699814 on OpenAlexvenueno aff
Deepalakshmi Shanmugam, K. Sundararajan

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicIndian History and Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsSoulIndependence (probability theory)Theme (computing)Subject (documents)Character (mathematics)Focus (optics)HEROPhilosophyMaterialismSociologyLiteratureEpistemologyArtComputer science

Abstract

fetched live from OpenAlex

Ratan Rathor is the main character of Arun Joshi's third book, The Apprentice (1974). He is an original soul caught in a materialistic and urbanized society. The hero’s self-analysis is the focus of the novel. Ultimately, he finds solace in daily devotion to the devotees, which serves as atonement for his actions. Gandhi's teachings, as well as those of Western and Indian philosophers, influences Arun Joshi. The novel’s central theme is a post-independence letdown. The unavoidable nature of evil returning to the evil-doer is the subject of the other dynamic leitmotif. Despite the perplexing surroundings, the focus always remains on the individual, who calculatingly chooses evil and then repents. Ratan looks like a victim of the contemporary world. This paper examines the notion of attempting to sort through the confusion that pervades modern life.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.012
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0120.010
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.228
Teacher spread0.209 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of English LanguageSame topicIndian History and PhilosophyFrench-language works237,207