Challenging practices: experiences from community and individual living lab approaches
Bibliographic record
Abstract
In this article, we examine a change initiative designed to involve households in testing ways to transform two everyday practices ‒ heating and doing laundry. The research design included an examination of the challenges of changing practices either in a setting that fosters collective engagement or with individual households. Two different types of living labs were carried out simultaneously in eight European countries in Autumn–Winter 2018. We reflect on differences in results in terms of both changes in practices and the experiences of participating households that we argue can be at least partially attributed to householders’ engagement in different types of living labs. We discuss the implications of an individual-focused vs. community-oriented approach for change initiatives seeking to challenge social norms for sustainability transitions, concentrating in particular on differences in the nature of participants’ engagement and their willingness and ability to challenge routine practices. This is complemented by analytical reflections on the differences in design, interaction, and performance between the two types of living labs. We show that an explicit focus on collaborative engagement in living labs can produce results that reflect shared experiences, community support, challenging established norms, and collective commitment toward change.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".