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Record W2742261682 · doi:10.3968/9605

Human beings create their own destruction: Critical discourse analysis of Once Upon a Time by Nadine Gordimer

2017· article· en· W2742261682 on OpenAlexvenueno aff
Muhammad Zohaib Khalil, Muhammad Ehsan, Khalid Mehmood, Hina Zainab, Farwa Azam

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Educational Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsScrutinyRace (biology)RacismGender studiesSociologyCriminologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The prezzie study is a Critical Discourse Analysis of Nadine Gordimer’s short story “Once Upon a Time”. The idea is to spotlight that is in Nadine Gordimer’s mind how human beings create their own destruction. Gordimer’s work is full of racial discrimination and social insecurity in the people of the South-African society and this insecurity and racial discrimination show how insecure they people are. The study is the scrutiny of Ruth Wodak Model. Research explores the human race is responsible for creating its own devastation. Moreover, the study tells us how we prepare ourselves in the danger of insecurity and how we can prevent ourselves in such circumstances.

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)0.015
metaresearch head score (Gemma)0.019
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 score0.020
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0200.057
Scholarly communication0.0140.018
Open science0.0020.008
Research integrity0.0050.010
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 head0.024
GPT teacher head0.432
Teacher spread0.408 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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