Learning Clothing Repair Strategies from Soviet and Italian Housekeeping Encyclopaedias for Women Published in the 1960s
Bibliographic record
Abstract
Research on clothing repair is primarily Western-centred, and it is rarely considered that non-Western perspectives on clothing repair can bring attention to issues that can be overlooked in the Western sustainability discourse.At the same time, there are societies, such as Soviet ones, where from the 1960s until the disintegration of the Soviet Union in 1991, homemaking practices were promoted as a form of consumption pleasure.Such strategic efforts led to the popularization of sewing skills, allowing for the successful transformation of collective conventions rapidly and on a societal scale (Kucher, 2024;Golubev and Smolyak, 2013;Gerasimova and Tchouikina, 2009).Therefore, by asking how such alternative approaches to clothing consumption were actuated and how they can influence the ways in which consumers engage with mending and other mending-related practices, the present article will compare Soviet and Western mending histories through the analysis of 45 housekeeping encyclopaedias for women.These encyclopaedias provided guidance on various activities traditionally associated with the home and creative endeavours in Soviet states and Italy in the 1960s.The study illustrates two fundamentally different approaches to economic organization which have resulted in different ideologies of consumption, highlighting how Soviet state repair strategies from the past can inform the current debates about sustainability in fashion, ultimately suggesting that the teaching and learning of mending should be re-integrated into school education.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".