Tilling the Field of Rural Arts Education in Canada: The Role of Intermediary Organizations in Brokering and Supporting Partnerships
Bibliographic record
Abstract
Intermediary organizations (IOs) are described across sectors as third-party entities that broker, guide, support, and evaluate partnership initiatives. This multiple-paper dissertation examines the work of IOs in supporting arts education partnerships in rural communities. The introductory chapter discusses key concepts underlying the study (e.g., intermediary organizations, rural, and arts education) and situates the work of IOs in developing rural arts ecosystems and arts education. The paper presented in Chapter 2 built a conceptual framework of arts education IOs through a cross-sector review of the literature. IOs are conceptualized through the framework as performing four major functions in support of arts education partnerships: assembling key stakeholders, supporting the aligned activities of partners, advancing policy and advocacy work, and mobilizing resources. The paper in Chapter 3 first identified the types of organizations that exist to support rural arts education in Ontario and Quebec through an environmental scan. Then, the resulting 83 organizations were analyzed for their potential to act as IOs through the application of an intermediary checklist, derived from the conceptual model in Chapter 2. The paper in Chapter 4 tested the conceptual model produced in Chapter 2 through an in-depth case study of one IO that supports rural artists-in-schools partnerships in Quebec. In addition to the four main functions of the conceptual framework, the function of sustaining artists-in-schools partnerships manifested in the work of the IO. The paper in Chapter 5 then consisted of a multiple-case study of how five IOs operate to support rural artists-in-schools partnerships in Ontario and Quebec. The data in Chapter 5 were collected, analyzed, and represented using a hybrid research approach based in playfulness. The findings are presented in a board game format with implications for IO practice, research, and knowledge mobilization. The concluding chapter summarizes the contributions and findings of all four papers, alongside implications and recommendations for IOs, researchers, policymakers, and other rural arts education stakeholders. In essence, this dissertation addresses the lack of rural arts education research in Canada, while contributing to practical knowledge on how IOs operate to support partnerships in the field.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".