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Record W3005294168 · doi:10.4000/rh19.6627

Ambivalences libérales : l’Anti-Corn Law League et la « democracy », 1838-1848

2019· article· fr· W3005294168 on OpenAlexaff
Hugo Bonin

Bibliographic record

VenueRevue d histoire du XIXe siècle · 2019
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceDemocracyLeagueArtLawPoliticsPhysics

Abstract

fetched live from OpenAlex

Afin d’éviter une lecture anachronique de l’émergence des démocraties libérales, il est nécessaire d’étudier l’histoire spécifique du mot « democracy ». Si certain.e.s se sont penché.e.s sur le cas britannique, peu ont porté une attention spécifique au mouvement libre-échangiste des années 1840 et à l’Anti-Corn Law League (ACLL). À travers une analyse des journaux, pamphlets et discours de la Ligue, nous examinons son double positionnement à la fois antiaristocratique et anti-démocratique. Bien qu’originale sur certains points, la critique du caractère élitiste des Corn Laws n’implique pas une valorisation de la « democracy » chez les libre-échangistes. Ce n’est que dans le contexte électoral que l’ACLL s’approprie positivement le terme. Mais, loin d’une affinité particulière entre libéralisme et démocratie, il ressort de cette étude le portrait d’une organisation aux positions équivoques, et en cela représentative de l’imaginaire politique victorien et ses rapports avec la démocratie.

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)0.002
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 score0.116
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.012
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.212
Teacher spread0.196 · 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
Published2019
Admission routes1
Has abstractyes

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