Responding to Ethical Dilemmas Through Adaptive Caring Work: Evidence From a Social Enterprise
Bibliographic record
Abstract
In recent years, increasingly rapid changes in the external environment (e.g. health crises) have created ethical dilemmas for Social Enterprises (SE) such as to respond to rapidly shifting needs in the communities they serve. Previous studies show that such external shifts create ethical dilemmas for SE decision-making contingent on their ethical stand, commitment to their social mission, conflicting demands from multiple stakeholders, and the organizational capacity to respond. In this study we set out to learn how a SE can respond to significant increases in the number of people who need their services while addressing considerable changes in the services they need? We conduct an in-depth longitudinal case study of a SE that has responded to the increasing and changing needs of its beneficiaries over a 30-year period. By adopting a sensemaking lens, we developed an inductive model of the ongoing “Adaptive Caring Work (ACW)” that our focal organization engaged in as an iterative process. ACW consists of three care sensemaking mechanisms and two mechanisms for the enactment of care. The model also illustrates that ACW is engaged by the SE to address not just externally-created ethical dilemmas but also the unintended consequences of their own caregiving. The study contributes to the ethics of care literature and to the emergent ethics as sensemaking literature.
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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 | 0.034 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".