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Record W7024735045

Straight on 'til mourning: death in Victorian and Edwardian children's literature

2021· dissertation· en· W7024735045 on OpenAlexaboutno aff

Bibliographic record

VenueCardinal Scholar (Ball State University) · 2021
Typedissertation
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)AdventureContext (archaeology)ReignGriefVictorian eraWhite (mutation)
DOInot available

Abstract

fetched live from OpenAlex

In contrast to sanitized portrayals of death today, ideas and behaviors surrounding death permeated Victorian and Edwardian culture in Britain. The Industrial Revolution brought disease and dangerous work conditions to England, which elevated mortality rates, especially among children. The middle- and upper-classes engaged in elaborate and expensive funerary practices, while the working-class rarely had the time or money to openly grieve for extended periods of time. No matter the economic or social standing, death practices presented the dead in open caskets, usually in the home, and grief was not hidden as it often is today. Children in these years, spanning from the beginning of Queen Victoria’s reign in 1837 and ending with the start of World War I in 1914, were familiar with the physical aspect of dying. Authors of these periods sometimes used this physical aspect of dying as a vehicle for presenting the act of psychological death in their stories for children. J.M. Barrie’s Peter Pan and The Little White Bird are placed in historical context and analyzed to understand how Barrie used physical death to represent psychological death, or loss of childhood, in his works. I include examples of Victorian authors Charles Kingsley, who wrote The Water Babies, and Lewis Carroll, author of Alice’s Adventures in Wonderland to suggest Barrie’s use of physical and psychological death was not an uncommon theme in Victorian and Edwardian children’s novels.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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