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Record W4392960350 · doi:10.1080/1362704x.2024.2326330

Fabrics of Memory

2024· article· en· W4392960350 on OpenAlexaboutno aff
Emma Windsor-Liscombe

Bibliographic record

VenueFashion Theory · 2024
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This article seeks to further examination of clothing with respect to associated experience and memory. The focus is the historical and personal narrative of one sweater worn by a young Canadian, Leonard McCann (1927-2015), in a Japanese internment camp during WWII. Tracing the travel of the sweater from Canada via the Red Cross to Manila, increases awareness of the potential significance of apparently mundane items of clothing. The manner in which such items can contain or embody memory, beginning at the time of their making, enables study of one moment within a broader history of clothing. The story of McCann’s sweater moreover enables investigation of the connection between clothing and trauma. Indeed, it is hoped that this study can stimulate further consideration of the curation of such clothing so that the value of seemingly ordinary garments becomes established in the discourse of fashion theory and history. Relating both the familiar look of the sweater and the narratives attaching to it, reveals a sense of living memory that also permits understanding of complex socio-political conditions. Perhaps this approach can augment empathetic as well as critical praxis in the discipline.

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)0.002
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.033
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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
Published2024
Admission routes1
Has abstractyes

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