Bibliographic record
Abstract
The archivists and collection managers who work in fashion collections may stay quietly behind the scenes, and yet their dedication to preservation and access is crucial for the public and for scholars and artists seeking knowledge and inspiration.This interview explores the rich collaboration that occurred from 2018 to 2019 at the Metropolitan Museum of Art between Ellen Sampson, a visual artist and material culture scholar, and Elizabeth Randolph, at the time the collections manager of the Costume Institute.The two conservators approached Sampson's practice-based fellowship project, "The Afterlives of Clothes," with different aims; Sampson was an artist and scholar intrigued by the often disregarded, stained, and dirty objects in a collection renowned for its pristine couture, while Randolph, with her near photographic memory, knowledge of the collection, and efficient organizational skills, facilitated access to even the smallest handkerchief.And yet, through this busy process of finding, selecting, pulling, examining, photographing, and putting away objects, moments of poignancy and loss invaded their daily work, reminding both Randolph and Sampson of the power of clothes and the memories they invoke.Their conversation reflects on this creative process in one of the world's preeminent fashion collections.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".