Happy Accidents and Bureaucratic Debacles: New Ways of Working Towards Impact
Bibliographic record
Abstract
Over the last decade, culture sector researchers have aimed to expand our understanding and measurements of impact assessment. In this paper, the authors take a deep dive into the happy accidents that create new ways of managing and working at several creative hubs in Canada. These happy accidents influence governance structures and practices these spaces take up but also help leaders and facilitators to respond to emergent needs in the cultural communities they seek to support. Drawing from two rounds of interviews and field research over the last four years, we compare seven creative hubs across Canada that have transformed their approaches to leadership and community engagement. We consider, first, how three types of creative hubs talk about their visions and values, and then how they operationalized those values in the spaces they are charged with activating. We look at two typical non-profit organizations (Tett Centre, National accessArts Centre), three social enterprises (cSpace, Culture Link and Artscape) and two outliers (UKAI, BC Artscape - the latter defunct). Many of these organizations face challenges as regional operations. Some explicitly aim to ameliorate the legacies of colonialism, discrimination and lack of representation in the culture sector. UKAI and BC Artscape are particularly interested in disrupting economic valuation frameworks by putting feelings and compassion at the centre of their operations. Others are compelled by more responsive but still competitive incubators for precarious creative workers, including cSpace and CultureLink. Consequently, we ask: in what ways is each hub ‘successful’ on their own terms, and which are useful exemplars to others? We look at each organization’s narratives of impact, process and affect emerging from overcoming challenges that include the global Covid-19 pandemic, bureaucratic debacles such as offering a disability organization a heritage site that is only accessible by stairs, and a series of awkward missteps and honest efforts towards decolonization.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.038 | 0.173 |
| Scholarly communication | 0.046 | 0.030 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".