Bibliographic record
Abstract
“Affect and Sensation” brings together cyanotypes and text from the practice-based project “The Afterlives of Clothes” to explore the sensory and emotional effects of archival fashion research. Addressing the ways that imperfect garments make the absent bodies of those who used, made, and repaired them present for us, the works are a call to engage with the intricacies of wear, gesture, and trace. Initially developed during a fellowship at The Costume Institute of the Metropolitan Museum of Art and later a residency at Bard Graduate Centre, the broader project asks how, in a field where absent bodies and narratives are already understood as problematic, presenting the traces of use might re-contextualize objects which would otherwise be excluded from view. Focusing on accessories, objects which Jones and Stallybrass term “detachable parts” of the self (2001b: 116), the images and writing draw upon a methodology that combines archival research with auto-ethnographic writing, image, and filmmaking to explore the embodied and bodily experience of researching imperfect garments in museum archives. Presenting archives as repositories of affect, labour, emotion, and bodily trace, they ask how ideas of affect and containment might shed light on the encounter with archival garments. This project presents garments in archives as both containers and producers of affect — an affect that, in part, stems from the bodies that wore and made them, but also from the multiple meanings that they acquire through accession, storage, conservation, and display.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".