Intro to special issue
Bibliographic record
Abstract
In the first of this double issue, we grounded the collection in Mass Culture’s Research in Residence: Arts’ Civic Impact (RinR) project. Aimed at creating a suite of impact measurement frameworks for arts organizations to assess where and how their work has impact, the SSHRC- and Mitacs-funded project embedded four individual graduate student researchers and one team of two graduate students—all from different post-secondary institutions—in arts organizations across the country. Also supported by a group of arts funders comprising an advisory, this first-of-its-kind initiative has had impressive impacts of its own in both the academic and culture sector spheres. Since publishing our first collection of articles, the landscape has changed. RinR’s graduate student researchers have all moved on in some way, be it finishing a master’s degree and starting a doctorate, finishing a doctorate and moving into a post-doctoral fellowship, finishing graduate school and working within an academic institution, and even continuing with their studies while starting a family. Other people involved in RinR have likewise changed jobs or even left the arts sector or academia altogether. Assembling this second issue has afforded us, the co-editors, some very welcome reflection on the project, the relationships we built through it, and how it continues to shape both our individual careers and perspectives on the arts’ civic impact.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.290 | 0.172 |
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".