White subjects: domestic science in the colonies and other places
Bibliographic record
Abstract
Classism is the most obvious ‘ism’ to plague the domestic science story. The domestic science movement was undeniably, to some extent and in certain quarters, about putting working- class women in their place. Imposing middle- class values on ‘an unruly, unkempt and ultimately unfit working class’ was a poorly thought out route to solving many problems of industrialisation and urbanisation: crime, drinking, poor nutrition, high infant mortality. But classism and sexism are linked to other ‘isms’. This chapter focuses on racism, imperialism and colonialism as creeds that have done their part in afflicting the domestic science movement. The chapter is also about the wide reach of the Euro- American ideology and practice of home science: how it was exported to other places, including Japan, Canada, New Zealand and other territories of what used to be the British Empire. The story in this chapter features a multi- faceted cast of characters: two Japanese women advocates of household science, Sumi Miyakawa and Hideko Inoue; the British household scientist Alice Ravenhill (again); a wealthy Canadian called Lillian Massey Treble; two clever Canadian food chemists, Annie Laird and Clara Benson; three British women who developed household science in New Zealand, Winifred Boys- Smith, Helen Rawson and Margaret Dyer; and two very different male characters, John Studholme, a philanthropic landowner, who thought women needed to be educated for their work at home; and an enthusiastically reformist Indian royal, the Maharaja of Gaekwad, who wanted a scientific woman to modernise his palaces. Household science was nothing if not versatile in adjusting to different cultural contexts. However what was versatile could also be inflexible. The same Euro- American- derived values and practices didn't necessary agree with the habits of the cultures into which attempts were made to insert them. Why whiteness? In 2001 a Canadian home economics teacher, Mary Leah de Zwart, was asked a testing question by one of her students: ‘White flour, white sugar, white sauce, white table manners, why is it that everything we do is white ?’ The student might have added to her (it was almost certainly a her) list of white subjects the overwhelming emphasis on whiteness and how to achieve it that has habitually haunted the laundry sections of domestic science manuals and classes, and that more than linger in our consumer industry today. Why should everything be white? What's wrong with off- white, grey, brown or even black?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".