MétaCan
Menu
Back to cohort
Record W2904574200 · doi:10.26522/vp.v15i2.2084

Le « Frankenstein intérieur » : la malignité envers soi dans Tomber sept fois, se relever huit de Philippe Labro

2018· article· fr· W2904574200 on OpenAlexaffvenue
Rosanne Abdulla

Bibliographic record

VenueVoix Plurielles · 2018
Typearticle
Languagefr
FieldArts and Humanities
TopicFrench Literature and Criticism
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Les représentations artistiques et littéraires de la malignité la présentent souvent comme ayant une cible extérieure à soi ; notre analyse abordera plutôt une intériorisation d'un tel antagonisme. L'autobiographie de Philippe Labro, Tomber sept fois, se relever huit (2003), raconte la dépression nerveuse dont le narrateur souffre pendant onze mois. Nous examinerons le discours dépréciatif avec lequel le Frankenstein intérieur, concept proposé par Edmund Bergler, est représenté, ainsi que le rôle clé que joue ce monstre interne chez le personnage déprimé. Les connotations négatives autour des troubles mentaux dans la société contemporaine poussent le narrateur à intérioriser les sentiments de honte qui l'assaillent, jusqu'au point de vouloir masquer sa souffrance devant autrui. Les multiples niveaux du récit de Labro servent ainsi à distinguer l'écrivain du texte de son personnage déprimé, et à créer une distance narrative qui sera essentielle dans sa réflexion sur le processus évolutif de sa guérison.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.017
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.019
Scholarly communication0.0060.006
Open science0.0000.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueVoix PluriellesSame topicFrench Literature and CriticismFrench-language works237,207