Participatory Governance and Community-Based Research at Mass Culture
Bibliographic record
Abstract
This article uses the national arts research network Mass Culture (MC) as a case study for assessing the strengths and limitations of participatory governance and community-based research for reimagining and enacting better futures in the Canadian arts sector. MC is currently the only digital network that takes such an approach to promote the equitable mobilization of arts research in Canada, which falls in line with broader trends and values associated with the participatory turn of cultural policy. At MC, this orientation is first reflected in the governance structure, which grew out of both grassroots processes and formal consultations involving key actors in the Canadian arts community. Here, I draw inspiration from Rosenau’s (Rosenau & Czempiel, 1992) definition of governance to refer to MC’s system of rule, which includes informal mechanisms such as intersubjective meanings, along with formally sanctioned regulations such as charters, terms of reference, etc. MC’s approach is also activated by the methods through which it designs, implements, and evaluates cross-sectoral collaborative projects at the national level. By experimenting with various community-engaged methods tailored to each of its initiatives, MC seeks to build the relational and data infrastructures that are needed to ensure that the research it produces is both relevant and easily accessible to potential users, from practitioners, artists, academics, arts funders, and policymakers, to those working at the intersection of several professional roles. By providing an in-depth account of MC’s emergence as a networked organization and by elaborating on its community-based approach to research, this article aims to contribute new knowledge about the value of various models of collaboration in the fields of cultural policy and cultural management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.081 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".