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Record W4285118808 · doi:10.36253/978-88-5518-565-3.17

Unlikely followers of fashion? Dressing the poor in late medieval Bruges

2022· book-chapter· en· W4285118808 on OpenAlexfundno aff
Peter Stabel

Bibliographic record

VenueFirenze University Press eBooks · 2022
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
FundersLunds UniversitetUppsala UniversitetUniversity of TorontoUniversity of OxfordUniversity of Cambridge
KeywordsEliteTypologyFifteenthClothingPovertyIdentity (music)Element (criminal law)Urban sociologySociologyPeriod (music)EconomyPolitical scienceHistoryLawEconomicsSocial scienceArtAestheticsAncient historyAnthropology

Abstract

fetched live from OpenAlex

Surprisingly little is known about the way the poor strata of urban society in the late medieval period used dress to express social identities. Systematic empirical data have not been available, and sources tend to illustrate the opinion of the elites about poverty. Through the analysis of cloth distribution by charitable institutions and, above all, of a unique set of inventories for fifteenth-century Bruges, it becomes clear that dress was not only an important element in the material culture and the construction of social identity of the poor, but that instead of being a passive player depending on charity and alternative commercial circuits, the poor used dress to conform to fashion cycles set by the wealthier groups in urban society. In late medieval Bruges, they were wearing the same typology of dress, the same colours and the same fabrics, displaying in this way a willingness to participate and invest in fashion cycles. In assessing both urban economies and social dialogue, scholars should therefore not focus on elite demand alone.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

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 studies0.0040.007
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.190
Teacher spread0.148 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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