How are arts organizations responding to critique in the digital age?
Bibliographic record
Abstract
Purpose – Online communications have made critical reviews widely accessible, enabled a broader range of opinions to be heard, and led to increased critical dialogue among audiences. The purpose of this paper is to investigate how arts organisations’ strategies for engaging with critique have evolved in the digital age. Design/methodology/approach – This paper uses a content analysis of the online presence of 45 organisations. Based on the results, the organisations are classified into three different strategic approaches. The organisations’ publicly available financial data are analysed to explain differences in the choice of strategic approach, and specific cases are used to better understand their strategic execution. Findings – Organisations are engaging in three primary strategic responses: ignoring outside critique, presenting only positive reviews and ignoring or “spinning” negative reviews, and presenting all critique regardless of source or valence. The financial analysis suggests that the choice of strategic response varies across organisations of different sizes and approaches to advertising. Case analyses suggest that the strategy of presenting all critique has the potential to deepen audience engagement and value. Originality/value – Previous research on critical reviews has focused on traditional media and the importance of the professional critic. This paper is the first to examine strategies for coping with the complex, multiple-voiced nature of critique in the current online environment. The findings suggest questions for future research and provide initial guidelines for organisations in determining a strategic approach to responding to critique.
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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 | 0.029 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".