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Record W2796297493 · doi:10.3917/pro.363.0080

Adaptation, not fossilization

2018· article· fr· W2796297493 on OpenAlexaff
Hartmut Rosa, Claire Chimelli

Bibliographic record

VenueRevue Projet · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPhilosophyPhysics

Abstract

fetched live from OpenAlex

professeur à l'université Friedrich Schiller de Iéna et directeur du Max-Weber-Kolleg à Erfurt (Allemagne).Il est notamment l'auteur du livre « Accélération.Une critique sociale du temps » (2010, 2013) à La Découverte.D'où vient la crainte farouche que suscite chez certains l'arrivée de personnes migrantes ?Pour le sociologue Hartmut Rosa, la peur de l'autre résulte notamment du fait que l'on ne se sent pas soi-même reconnu et considéré.'est vrai : si nous les laissons entrer chez nous et si nous acceptons d'avoir affaire avec eux, les réfugiés vont nous changer ; nous nous transformerons et serons bientôt différents de ce que nous sommes aujourd'hui !En ce sens, ce serait certainement une erreur de prétendre que la question des réfugiés ne représente pas un défi pour notre société.Mais le reconnaître ne signifie en aucun cas faire tout notre possible pour rejeter ce défi ou le minimiser.Dans les propos qui suivent, je vais esquisser deux réactions possibles. TOUJOURS PAREIL. LA RÉPONSE POPULISTE DE DROITEUne étude récente de la sociologue Arlie R. Hochschild met en lumière C.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.021
Scholarly communication0.0040.007
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0160.002

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 head0.265
GPT teacher head0.423
Teacher spread0.157 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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