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Record W2031248182 · doi:10.4000/belphegor.387

De Kensington Gardens à Neverland : Peter Pan et ses territoires

2011· article· fr· W2031248182 on OpenAlexvenueaboutno aff
Caroline Orbann

Bibliographic record

VenueBelphégor · 2011
Typearticle
Languagefr
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Si James Matthew Barrie n’a eu de cesse de transformer le personnage et les aventures de Peter Pan, il en a également modifié le décor. En effet, l’île de Neverland s’est substituée à Kensington Gardens, territoire originel de Peter dans The Little White Bird (1902), justement sous-titrée Adventures in Kensington Gardens. L’objectif du présent article est de comprendre de quelle manière l'auteur a construit les territoires au fil des réécritures. Le choix du motif insulaire apparaît comme le résultat d’une forte influence des romans d’aventures dont Barrie était un lecteur assidu. La construction de Neverland semble s’être opérée en plusieurs étapes dans le processus d’écriture, l’auteur ayant ébauché la figure de l’île dans des oeuvres antérieures. Barrie s’est finalement approprié cette topographie ilienne, qui devient le lieu de l’atemporalité et de l’amnésie, dans Peter Pan bien sûr, mais également dans The Admirable Crichton (1902) et Mary Rose (1920). Pourtant si la transposition spatiale de Kensington Gardens à Neverland semble changer radicalement le cadre des aventures de Peter, il n’en subsiste pas moins des éléments communs qui mettent en lumière certains aspects de la poétique de l’espace Barrien.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.005
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.029
GPT teacher head0.228
Teacher spread0.199 · 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
Published2011
Admission routes2
Has abstractyes

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