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Record W4391757271 · doi:10.32920/25209353.v1

Discursive Constructions of Death and Dying in Childhood Films

2024· preprint· en· W4391757271 on OpenAlexaff
Rose Bonello

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood DevelopmentUniversity of Guelph-Humber
Fundersnot available
KeywordsAmbiguityConstruct (python library)Discourse analysisSociologyGender studiesPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Death and dying is often an uncomfortable and controversial topic within Western society. The ambiguity and often silent nature surrounding death and dying led to the question of how death is depicted in childhood Disney-Pixar films. This paper begins with a review of the relevant literature surrounding death and dying in childhood. Then, through the theories of the new sociology of childhood, neoliberalism and developmental theory, an interdisciplinary approach was used to explore death and dying in 33 Disney-Pixar films. Using critical discourse analysis, the audio, visuals, and close-captions of each film were analyzed for discourses of death and dying. The findings explain how Disney-Pixar films discursively construct death, and how these messages may be interpreted by children, and in particular, sick children. The discussion and findings presented in this paper can support Child Life Specialists (CLS) in their practice when caring for children experiencing end of 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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0080.021
Scholarly communication0.0090.006
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.327
Teacher spread0.297 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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