Doing laundry in consumption corridors: wellbeing and everyday life
Bibliographic record
Abstract
In this article, we explore the possibilities for a transformation toward more sustainable energy usage by engaging with mundane activities, such as doing the laundry. Across European households, laundry practices rely on social norms and material arrangements, which makes these practices rather “sticky” and resistant to change. Through the lens of consumption corridors, and accounting for wellbeing in relation to the basic needs of participation, health, and autonomy, we study laundry practices and their transformation in 73 Finnish and Swiss households that took part in a challenge to reduce their weekly wash cycles by half over a four-week period in autumn 2018. By using both qualitative and quantitative data, we analyze how participants defined minimum and maximum standards for cleanliness and convenience, for themselves and for others, over the course of the challenge period. Specifically, we consider how the sequencing of tasks associated with “doing the laundry” changed, as well as the significance of social relations and sensations in representations of social norms. The participants’ experiences helped uncover how setting limits toward consumption corridors can be achieved, whereby reductions in consumption can result in sustainable wellbeing.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".