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Record W4362581506 · doi:10.29173/cons29501

Joseph II’s 1782 Edict of Toleration for the Jews of Lower Austria and its Economic and Secular Underpinnings and Effects

2023· article· en· W4362581506 on OpenAlexvenueno aff
Emma Trevor

Bibliographic record

VenueConstellations · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsTolerationJudaismLegislationEmperorPoliticsLawState (computer science)Jewish studiesSociologyPolitical scienceEconomic historyHistoryPolitical economyAncient historyArchaeology

Abstract

fetched live from OpenAlex

Joseph II’s 1782 Edict of Toleration is an important piece of legislation within Austrian and Jewish history. The Edict was a series of statements issued regarding the inclusion of Jewish citizens into larger towns, marking what was permitted, what was to change, and what was prohibited. Created by Holy Roman Emperor Joseph II, son of the notoriously anti-Jewish Empress Maria Theresia, the legislation was institutionalized in order to make Jewish people more useful economically to the state by granting them access to cities and towns, Christian schools and universities, and by allowing them to set up their own factories. It created secular subjects to achieve economic gains. The Edict can be analyzed for its economic underpinnings and effects, which can be further examined through a micro- and macroscopic lens, as well as viewing the role it had in promoting the toleration and assimilation of Jews. The Edict, despite its failure to achieve its steep economic goals or to fully assimilate Austria’s Jewish community, nonetheless is key to understanding the political, economic, and religious climate of Lower Austria at this time.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.011
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.326
Teacher spread0.298 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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