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Record W2182075676 · doi:10.5539/ells.v5n4p31

Charles Dickens’s David Copperfield: New Critical Reconsiderations

2015· article· en· W2182075676 on OpenAlexvenueno aff
Ali Albashir Mohammed Alhaj

Bibliographic record

VenueEnglish Language and Literature Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Policy, and Dickens Studies
Canadian institutionsnot available
Fundersnot available
KeywordsKindnessLaughterPortraitPopularityLiteratureHistoryArtArt historyPhilosophyLawTheologyPolitical science

Abstract

fetched live from OpenAlex

The current study aims at reconsidering critically Charles Dickens’s David Copperfield. Charles Dickens is perhaps the greatest—if not the most perfect—of Victorian story-teller whose works have become synonymous with Victorian England. Many of his novels came out in monthly installments and were awaited by his readers eagerly. His popularity lay in his ability to write gripping, sentimental stories filled with memorable characters. On a more serious level, his novels are a detailed account of both the good and bad sides of Victorian life. In the semi-autobiographical David Copperfield, the author paints a graphic picture of the living condition of the urban poor. He also denounces the exploitation of children by adults and the cruel competitive nature of Victorian society. To conclude, characters such as Micawber (a portrait based on Dickens’s own father) has passed into folk lore and become household names, used by people who have never read a Dickens novel in their lives. Also, the writer uses too much black paint. However, he wanted to raise kindness and goodness in men’s hearts, and he used tears and laughter to reach his aims. He probably brought a little improvement in some condition, but very often, he failed to do so.

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)0.008
metaresearch head score (Gemma)0.017
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 score0.125
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0220.065
Scholarly communication0.0130.014
Open science0.0030.006
Research integrity0.0090.017
Insufficient payload (model declined to judge)0.0050.001

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 head0.051
GPT teacher head0.379
Teacher spread0.329 · 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
Published2015
Admission routes1
Has abstractyes

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