Fashion as Mood, Style as Atmosphere: Literary Non-Fiction on SSENSE and London Review of Looks
Bibliographic record
Abstract
Amongst the discourses that describe, construct and critique fashion, one form, which we might deem a kind of literary non-fiction that attends to how fashion, dress and social moods entwine has thus far largely escaped scholarly notice. While fashion criticism often attends to the “moods” a collection addresses, this mode of writing, primarily circulated on digital platforms, considers how clothes place us as social, affective beings within culture and everyday life, and elucidates the ways fashion interacts with one’s person in fanciful and sensory ways. Written in response to its writer’s perspective and experience, such writing uses literary devices to render palpable what clothes do, how it feels to long for a garment or lose one’s taste for dress. In this way, it helps us to understand what an attunement to fashion and/or dress looks and feels like in practice. This chapter argues that this literary non-fiction writing on fashion and dress reveals how social moods and clothing interact. It primarily considers two contemporary examples: the fashion writing published by Montreal-based luxury e-tailer SSENSE and writer Ana Kinsella’s newsletter London Review of Looks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".