Moral judgements as organizational accomplishments : insights from a focused ethnography in the English healthcare sector
Bibliographic record
Abstract
In this chapter, we aim to deepen our understanding of judgments in organizations. Whilst previous studies have underscored the situated nature of individual judgments exercised by e.g. leaders or managers, our research focuses on how judgments emerge as organizational responses to recurrently emerging moral dilemmas. Accordingly, we study a setting—decision practices in the English healthcare sector—where moral puzzles (to fund or not to fund healthcare for apparently atypical patients) demand ongoing attention and systemic handling. We conducted (and present findings of) a focused ethnography of the ways expert decision-making panels in three health authorities confronted, engaged, and coped with morally perplexed situations. The moral perplexity there lay in that panels were called upon to prudently and demonstrably determine whether a particular patient deserved or not exceptional investment; and do so by taking into consideration the healthcare needs and rights of all patients under the same health system. By adopting a practice perspective (Schatzki, 2002), we develop an analytical account of the effortful accomplishments (sociomaterial activities or intertwined “projects” in practice theory terms), which enabled the recurrent collective exercise of judgments in accordance with publicly recognizable moral expectations—namely notions of fairness. Our main contribution lies in conceptualizing the work of rendering moral judgments as organized pursuits possible and meaningful and hence in complementing current “ecological understandings” of individual judgment-making in organizations.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".