An artist-educator's role in community arts : integrating people of diverse backgrounds and ages
Bibliographic record
Abstract
This research focuses on teaching methods in community arts programs and projects integrating diverse people through collaborative work. Through giving "Art of Japanese Calligraphy" Workshops, I developed my teaching method focusing on creativity of personal expression through collaborative work. The documentary video of the project shows the active process of the participants' personal creations. The participants were children and adults, some with and some without disabilities. Three workshops were held at L'Atelier d'Artisanat du Centre-Ville Inc. in Montreal, and the participants expressed themselves in their collage artworks with Japanese calligraphy In the first and second workshop, they recreated their names and the natural environment by using the letters, paper and other materials on three pieces of large papers. In the third workshop, the participants collaborated with each other to add details to each other's artwork. In having two types of collaboration, I found that different benefits were achieved However, enabling people to communicate through art in both approaches allowed them to learn to respect and to honour each individual's idea.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".