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PUTTING ON A GRAVE FACE: THE TRANSITION OF MOURNING DRESS FROM 1900 TO 1920

2023· dissertation· en· W4389399035 on OpenAlexaboutno aff
Eli Bejin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsClothingFellEtiquetteQuarter (Canadian coin)Face (sociological concept)Period (music)HistoryVariety (cybernetics)ArtVisual artsAestheticsSociologyGeographyCartographySocial scienceArchaeologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Mourning dress during the nineteenth century had prescribed rules that were forced to change in the twentieth century due to societal pressures and changing values about death and mourning. The goal of this study was to examine the changes and the results of those changes in upper and upper-middle class mourning dress in the United States of America during the first quarter of the twentieth century. To do this, two garments believed to be mourning dress from the early twentieth century were examined and compared as a case study. Two questions were researched and answered in this study. How did mourning dress visually and materially change during the first twenty-five years of the twentieth century? Do the garments selected for study reflect those changes? Mourning dress changed visually to align with the trends in non-mourning fashion. Additionally, a wider variety of materials became acceptable as mourning crepe fell out of fashion and mourning etiquette became less formal. Overall, the garments selected for this case study did reflect the visual and material changes.

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: Other · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.089

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 studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.267
Teacher spread0.220 · 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
GenreOther

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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