Intro to special issue
Bibliographic record
Abstract
Intro to special issue How We Work With/in Culture Now: Reimagining Impact Assessment and GovernanceOver the last several decades, policymakers, funders, artists, and arts organizations alike have attempted to find ways to assess the impact of their investments in and support of the arts (e.g., Banks 2010; Dempwolf et al 2014; Essig 2018).Moreover, scholars and culture sector leaders have often noted that the sector tends to depend on a fairly narrow brand of research and evaluation practices to inform future directions.These approaches tend to be reliant on fundraising or marketing imperatives such as achieving a monetary goal for a revenue stream, or filling seats in performance halls, or to address the operational use of funding such as how many activities were conducted, and how many people were involved so that grant recipients (for example) might better account for public funds.But there is more to assessing impact than to report on outputs or aggregate numbers (e.g., Luka 2022).In 2020, while working with several policymakers and funders while having identified several scholars working on these kinds of questions including finding ways to ameliorate the dearth of qualitative impact assessment frameworks, Mass Culture decided to facilitate and develop a renewed level of collaboration across the arts community, arts funders, and academia to advance arts impact research in Canada.This initiative has resulted not just in the production of robust qualitative impact assessment frameworks but also a community of practice (e.g., Markham 2018; Wenger et al. 2002) that bridges between varied governance structures and practices to support cross-sectoral efforts to identify what the culture sector brings to society today.In 2020, Mass Culture convened a series of discussions that resulted in funding and participation commitments from several funder organizations (including Canada Council for the Arts, the Culture Statistics Working Group: Federal-Provincial-Territorial Culture and Heritage Table, Ontario Trillium Foundation, and the Toronto Arts Foundation) to support what became the Research in Residence: Arts' Civic Impact initiative (RinR) in 2021-22.i With the additional support of Mitacs ii funding, the project supported six graduate students as interns for RinR, followed by a successful request for 2022-23 for a Mitacs Postdoctoral Fellow to conduct further related research in cultural governance and creative labour.The RinR project's governance structure is illustrated below in Figure 1, including the six universities, six interns, and one postdoctoral fellow that participated (Carleton, Dalhousie, McGill, Toronto, and Winnipeg, and Emily Carr University of Art + Design's Aboriginal Gathering Place), as well as more than a dozen arts organizations.The researchers and their specific arts' civic impact area of focus were: Sydney Pickering, Indigenous Cultural Knowledge (Emily Carr); Emma Bugg, Climate and Sustainability (Dalhousie); Aaron Richmond, Health and Wellbeing (McGill); Shanice Bernicky, Diversity and Inclusion (Carleton); Audree Espada and Missy LeBlanc, Diversity and Inclusion (Winnipeg), and Laurence Deroin Dubuc, Creative Labour and Sustainability (Toronto).The latter Mitacs Postdoctoral Fellowship was held at University of Toronto Scarborough (UTSC), and hosted by Mass Culture, and their work appears in this special issue (Dubuc 2023).
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.333 | 0.258 |
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".