Doing the right thing? Managerial ethics in social engaged arts
Bibliographic record
Abstract
Who, if anyone, is responsible for ensuring arts organisations working in Social Engaged Arts do the ‘right thing’ in a ‘good’ way and how is it determined? Although established as a distinct field managerial ethics (and indeed business ethics) has rarely been applied to the study of arts and cultural organisations or their management. Using interviews and Mission/Vision analysis, this chapter applies a managerial ethics lens to the ethical decision-making and rationalities of the artistic and executive leads (the ‘managers’) and arts organisations in Northern Ireland who work in or deliver Social Engaged Arts (by their definitions: ‘community arts’ and ‘participatory arts’). It examines what they consider ethical concerns and what influences their ethical decision-making. In doing so, we articulate insights to the relational and values-based approaches adopted to decision-making, the ethics of self-exploitation and disillusion, and the ambiguous space between personal and organisational responsibility and politics. The work thus fills a gap in more traditional business ethics inquiry while at the same time opening up new considerations in the field of arts management, leadership and governance. It demonstrates how arts and cultural organisations and their managers in this context prioritise a sense of ‘common good’, their beneficiaries and artists over artform and pursuit of social justice. This work opens up debate and fuels understanding of the structural inhibitions around ideals of shared decision-making, collectivism and ethicality at individual, organisational and sector levels. In this way, it may enhance managers’ and organisations’ capacity to refashion ways to resist neoliberalism while also highlighting contradictions between ‘good’ governance, accountability and lived reality.
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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 | 0.041 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.140 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".