“All Kinds of Dirty”: Supermarkets, Markets, and Shifting Cultures of Clean
Bibliographic record
Abstract
The makers of CLR (the Calcium, Lime, Rust, cleaning product) assure us that there are “all kinds of dirty, one kind of clean.” One can feel confident that soap scum buildup and toilet bowl stains in the bathroom as well as the grease splatters and dried-on tomato sauce in the kitchen can be wiped away with the help of one yellow bottle. The pithy slogan asks us to be preoccupied by dirty in all its forms, without taking into account the many discourses of clean. This article concerns itself with the cult of “cleanness” and the ways in which it has taken hold of the imaginary when it comes to our bodies, the things we put into them, and the spaces we make use of and/or inhabit. I make particular reference here to the spaces in which we buy food, exploring various implications of the staging process enacted in the processing and display of foodstuffs. I set out to examine the ways in which clean is implemented and interpreted by and within two major sites of food shopping: the supermarket and the market.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.056 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".