Exploring Design for Repairability Guidance: Issues, Actions, and Outcomes
Bibliographic record
Abstract
Abstract Design for repairability is essential for improving product sustainability, yet current design guidance remains fragmented and frequently incomplete. This study systematically reviews 24 academic sources to identify and analyze existing repairability strategies, evaluating their actionability (what issues they address and what actions they propose) and evaluability (what outcomes they aim to achieve). Our analysis highlights substantial shortcomings. Existing strategies often fail to implement human-centered design actions and lack quantitative measures to evaluate repairability. Further, guidance is frequently fragmented, failing to link recommended design actions with specific repairability issues or outcomes. To overcome these limitations, our paper proposes a structured framework based on morphological analysis. By systematically combining identified issues, actions, and outcomes, the framework generates 462 distinct design strategies. The framework further outlines initial steps to incorporate contextual considerations, enabling designers to narrow down suitable strategies within the extensive solution space. Contextual factors influencing strategy selection include product-specific repair issues, designers’ responsibilities, and the skills and resources of the people repairing. This analysis and the proposed framework enhance the comprehensiveness and flexibility of design for repairability guidance, contributing to greater product sustainability. Future research will refine and validate the framework by explicitly defining contextual factors and applying them in practical case studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".