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Record W2161141851 · doi:10.3138/flor.22.003

Retirement Arrangements and the Laity at Religious Houses in Pre-Reformation Devon

2005· article· en· W2161141851 on OpenAlexaffvenue
A.D. Fizzard

Bibliographic record

VenueFlorilegium · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsCampion CollegeUniversity of Regina
Fundersnot available
KeywordsAllowance (engineering)PeasantAnnuityBequestClothingGentryEconomicsLife annuityDemographic economicsLawPolitical scienceFinanceOperations managementPension

Abstract

fetched live from OpenAlex

Most of us would probably think of retirement planning as a modern phenomenon; however, concerns about retirement were also very real to people of the late Middle Ages and the early modern period. They, too, sought ways to ensure that they would be able to lead reasonably comfortable lives once they had reached a point when they could no longer work. Peasant parents might sometimes strike maintenance agreements with adult children or other individuals who would be taking over their landholdings; wealthy widows might be able to rely on the income from their dower lands or the jointures arranged at the time of their marriages. Infirm members of the secular clergy could sometimes retire from their parishes with pensions. Another option for a wide range of individuals of different economic backgrounds was to obtain an annuity from or a corrody at a religious house. An annuity was an annual monetary payment, whereas a corrody was an annual allowance in kind—what Barbara Harvey has called a “bundle of privileges” or a “bundle of consumables”—providing for the food, shelter, and often clothes of the corrodian. Both annuities and corrodies could be purchased from or freely given by a monastery.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.021
GPT teacher head0.205
Teacher spread0.184 · 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

Citations2
Published2005
Admission routes2
Has abstractyes

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