Designing as trading-off: a practice-based view on smart service systems
Bibliographic record
Abstract
Posture-related problems, such as back pain, are an increasing global burden. They are deeply intertwined with how humans sit. While the information systems (IS) literature has been relatively silent on this matter, emerging literature in related disciplines has begun to attend to this problem by developing various artefacts. However, researchers have oftentimes done so by basing their artefacts on engineering rationales and attending only limitedly to the interactions between artefacts and humans. These interactions are crucial because data on posture is best collected by placing sensors on humans’ backs. This calls for considering and evaluating how bodies move in relation to sensors, the emotive reactions of humans to sensors and how humans make sense of recommendations emanating from underlying artificial intelligence (AI) technologies. We uncover what these considerations of human-centredness mean for designing smart service systems for posture management and suggest that a core consideration relates to trading-off possibilities of smart technologies and necessities emerging from practices. This study contributes to the body of knowledge on designing smart service systems and responds to calls for more IS research dealing with the prevention of chronic health conditions.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.085 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.006 |
| 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".