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Record W2222400110 · doi:10.4000/ifha.1118

KELLER, Katrin, Kleinstädte in Kursachsen. Wandlungen einer Städtelandschaft zwischen dreissigjährigem Krieg und Industrialisierung

2013· article· de· W2222400110 on OpenAlexaff
Guillaume Garner

Bibliographic record

VenueRevue de l’Institut français d’histoire en Allemagne · 2013
Typearticle
Languagede
FieldArts and Humanities
TopicMedieval European History and Architecture
Canadian institutionsInstitut d'Histoire de l'Amérique Française
Fundersnot available
KeywordsKATRINPolitical scienceArtPhysics

Abstract

fetched live from OpenAlex

L’histoire des villes allemandes à l’époque moderne a longtemps été victime à la fois d’une certaine négligence, au profit des périodes d’apogée et d’essor qu’auraient respectivement représentées le bas Moyen Âge et l’industrialisation du XIXe s., et d’une image négative : la ville allemande, en particulier la petite ville, serait l’incarnation de la stagnation, de l’immobilisme, voire du refus de toute innovation. La prise en compte des conséquences de la guerre de Trente Ans n’a fait que re...

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)0.001
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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.009
Scholarly communication0.0040.004
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.215
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

Citations2
Published2013
Admission routes1
Has abstractyes

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