Experiencing wellbeing at La Ruche d'Art: Methods and materials of an art hive
Bibliographic record
Abstract
Involvement in the creative arts has a sustained and positive impact on mental and social wellbeing. Adding a third space for arts-based social inclusion, community engagement, and service learning for university students, provides a powerful vehicle for civic exchange across diverse demographics. Over time, a community art studio, aka Art Hive, provides a platform for participatory practice research leading to social innovation. This workshop recreated in part, La Ruche d'Art (The Art Hive), a university storefront classroom and a public home place for residents in a low wealth neighbourhood in Montreal. A public home place is a protected safe space, both psychologically and physically, which invites community members to share their skills and develop their unique voices. The workshop introduced theories, methods, and materials used in the Art Hive. Attendees assembled small visual journals based on creative reuse principles while sharing stories related to the relevance and scope of these special third spaces. Concordia University‘s Art Hive launched in 2011 hosts a network of 100 Art Hives across North America and Europe. This workshop encouraged participants to consider developing an Art Hive in their workplace or community.
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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".