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Record W3192140765 · doi:10.21248/hgbll.2019.191

Rentengeschäft im spätmittelalterlichen Reval

2021· article· en· W3192140765 on OpenAlexaff
Steffen Schlinker

Bibliographic record

VenueHansische Geschichtsblätter · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMedieval European History and Architecture
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsCreditorEstateDebtorCapital (architecture)Property (philosophy)InstitutionOrder (exchange)Distribution (mathematics)PaymentActuarial scienceEconomicsDebtBusinessLaw and economicsDemographic economicsLawFinancePolitical scienceHistory

Abstract

fetched live from OpenAlex

This article examines the deeds establishing annuities in late-medieval Reval (Tallinn). In most cases, some clerical institution or the city council itself functioned as the debtor, promising a yearly rente to a creditor in exchange for a capital sum. Among the beneficiaries are numbered one or more natural persons as well as corporations such as churches. The deeds show that granting annuities was found to be useful in a number of different contexts. They could be employed to create a fixed annual income. They could also be used to gain spiritual merit, if a perpetual rent was created in order to benefit a church or hospital. They could be used as a form of payment for the life-time use of real property or to facilitate the distribution of an estate among the heirs. Of course, annuities were a risky proposition, a fact which generated a number of individual stipulations to cover various eventualities. The age and the number of beneficiaries may have influenced the level of the rente, which normally ranged between 6 % and 10 % of the capital invested.

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 score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.029
GPT teacher head0.195
Teacher spread0.166 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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