A Framework of Foreseen and Unforeseen Harms in Transformative Service Systems
Bibliographic record
Abstract
Transformative service systems (TSSs) are designed to uplift human well-being. Yet, paradoxically, by necessity and in design, TSSs can also generate unintended harms for system actors. Our conceptual paper builds on recent service literature, as well as that on unintended consequences from a range of fields, to advance an integrative framework of harms in TSSs. Through the enabling theory of the doctrine of double effect, our framework organizes harms in the transformative service context, identifying that unintended harms can be both foreseen and unforeseen. Additionally, we find that the mechanism underlying these harms is system emergence. Emergence arises from the relative complexity of the service system and the relative dynamism of the issue the TSS aims to address. Our framework demonstrates that greater service system complexity increases the likelihood of foreseen harms, while greater relative dynamism increases the likelihood of unforeseen harms arising. Furthermore, we show how these two factors combine to promulgate the emergence of harms. We find that in instances where harm arises, greater service system adaption is required to mitigate such harms. However, some TSS harms are an inevitable and unfortunate secondary outcome of doing good, and these harms necessitate acknowledgment and acceptance by service designers.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".