Bibliographic record
Abstract
In this contribution, Research in Residence (RinR) co-facilitators Mary Elizabeth Luka and Robin Sokolsoki host a conversation with members of the RinR Funder Advisory, addressing the dynamics of collaboration, impact assessment, and applied research in the Canadian culture sector, using RinR as a case in point. While projects and operational approaches that incorporate partnerships and collaboration have been encouraged and funded for many decades through programs such as the Digital Strategy Fund at the Canada Council for the Arts, or by the Social Sciences and Research Council of Canada through its suite of partnership grants, how funders collaborate or enable partnerships among themselves or more directly with sector organizations has been less supported or evident. Additionally, over the last decade, industry and scholarly researchers have repeatedly noted that the sector tends to depend on a narrow band of research practices to conduct impact assessments— primarily from financial or economic points of view—and thereby to inform future directions not just for the organizations but also for the sector. To respond to this situation, in 2020, Mass Culture convened a series of discussions that resulted in various levels of resource support as well as participation commitments from several funder organizations for what became the Research-in-Residence: Arts’ Civic Impact initiative in 2021-23. This article traces the snowball effect of bringing various levels of funders onboard for this project before turning to discussions of how the group worked together throughout the project, including key learnings shared across the funding ecosystem and into the sector.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.025 |
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".