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Record W2157955784 · doi:10.1093/geront/gnr149

Portraits of Aging Men in Late Medieval Italy

2012· article· en· W2157955784 on OpenAlexaff
Roisin Cossar

Bibliographic record

VenueThe Gerontologist · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPortraitConstruct (python library)PsychologyHealthy agingSuccessful agingGerontologyGender studiesHistorySociologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: This essay examines the human experience of aging in the distant past by investigating a group of aging men during the 14th century in an Italian city, Bergamo, using notarial "documents of practice" from that community. Studying the aging process and its effects on the lives of people in the medieval era has three-fold significance: it broadens our understanding of aging as a human construct and a human experience, challenges an antihistorical theory of aging, and reinforces the importance of studying the specific experiences of aging individuals in both the past and the present. DESIGN OF THE STUDY: A qualitative study. Methods of analysis include nominative linkage and an investigation of the physical effects of aging on an individual, as seen in the documents of 1 long-lived notary. RESULTS: Aging clerics and notaries in Bergamo took on positions of increasing authority in the church and related institutions in the last decades of their lives. IMPLICATIONS: The documented activities of a group of affluent men in 14th-century Bergamo suggest that although there was little recorded discussion of "old age" as a life stage in that community, for these men, aging was a real social process with both positive and negative impacts on their lives. Giving a human face to these aging men of the distant past models an approach to the study of the aging process that has relevance for both historians and gerontologists alike.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.255
Teacher spread0.190 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2012
Admission routes1
Has abstractyes

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