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Record W4232199961 · doi:10.3406/ahrf.2005.2755

L'administration locale en temps de crise : le cas de l'Isère en 1814-1815

2005· article· en· W4232199961 on OpenAlexaff
Marie-Cécile Thoral

Bibliographic record

VenueAnnales historiques de la Révolution française · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsInstitut d'Histoire de l'Amérique Française
Fundersnot available
KeywordsLocale (computer software)JacobinAdministration (probate law)Local governmentState (computer science)Political scienceGovernment (linguistics)Work (physics)Spanish Civil WarHumanitiesPublic administrationLawPoliticsArtFrench revolutionEngineering

Abstract

fetched live from OpenAlex

Marie-Cécile Thoral, Local Government in Time of Crisis : the case of the Isère Department in 1814-1815 Part of French territory, including Isère, was twice occupied by the Allies in 1814 and again in 1815. War and occupation had a major impact on local government, in terms of administrative staff and their work, and the relations between the state and its citizens. These crisis conditions provide an opportunity to assess the efficiency of the French administrative model set up by Napoleon on 28 Pluviôse Year VIII and exposed to the wear and tear of the effects of war and occupation. Study of local government in the Isère department during these troubled times shows that administrative continuity was ensured by the action of local officials (especially the mayors) and the participation of citizens, in particular local worthies. The administrative model that proved resilient in time of crisis was not the highly centralized Jacobin model, but rather that of a truly local administration, a model whose ability to weather the storm is proof of its efficiency.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.781

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.011
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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 designQualitative
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
Published2005
Admission routes1
Has abstractyes

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