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Record W7039254574

Les villes américaines d’après Simone de Beauvoir

2000· other· fr· W7039254574 on OpenAlexaboutno aff

Bibliographic record

VenueEl Servicio de Difusión de la Creación Intelectual (National University of La Plata) · 2000
Typeother
Languagefr
FieldSocial Sciences
TopicEducation, Innovation and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)ParadiseESPACEQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

En 1948, Simone de Beauvoir publie son journal “L'Amérique au jour le jour" où elle raconte sa decouverte -entre janvier et mai 1947- ues villes américaines. Au fil des jours, elle voit New York et Chicago, Washington et Santa Fe, New Orléans et San Francisco. Elle voit aussi les petites villes: Reno, Rochester, Charleston, les villages: Taos, Roxbury, Oberlin, les paysages de la Nouvelle Angleterre, du Texas, de la Californie. Et, bien sûr, beaucoup d’autres régions, villes et villages. Gourmande insatiable, elle ne veut qu’aucun “iieu priviligié interdit au touriste naïf" lui échappe. Arrivée dans une ville, elle se dit: comment y accéder?. Par où la prendre?. Que pourrai-je en saisir?. En partant, elle se demande toujours: cette ville ne m’a pas filé entre les doigts?. Avait-elle mieux à m’offrir?. Ses secrets n’étaient-ils que mirages?. Car “il y a dans son coeur l’anxiété et la gourmandise des nuits de Noël enfantines”. (Beauvoir, Simone de, “L’Amérique au jour le jour", Paris, Editions Paul Morihien, 1948, p.113) Comment être jamais certaine d’avoir tout connu, tout embrassé? Pareille ambition précipite Simone de Beauvoir dans un agenda infini: régions à parcourir, quartiers à visiter, êtres à connaître,conférences à faire, entretiens à mener, spectacles à jouir, partys à assister.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0550.000

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 head0.021
GPT teacher head0.299
Teacher spread0.277 · 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 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
Published2000
Admission routes1
Has abstractyes

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