Innovative Exemplars and Curriculum Created from Online Videos of Visual Artists in Greater Sudbury
Bibliographic record
Abstract
The article begins with a description of the award-winning online artists’ video project, 14 Videos of Visual Artists in Greater Sudbury, and concludes with a presentation of my pre-service BEd students’ creative use of this digital resource. The video series was conceived and created with the aim of filling a gap in materials that were sorely lacking to teachers of Visual Arts in Ontario. The video series includes Aboriginal, Métis, Francophone and Anglophone artists and highlights the artists’ interconnections with the local community. The streamed, linked and library-accessible videos (see http://www3.laurentian.ca/visual_artists/) served as inspiration for student teachers’ creation of their own innovative curricular exemplars. In the article, I describe the complex inner workings of the research project in order to establish a context for the students’ work. I show how the students were able to conceptualize curriculum through being able to better see what and how to teach through creating art and making exemplars in a variety of media. Using the artists’ work as a catalyst, the students worked in groups, selecting artists whose artwork spoke to them while creating exemplars and co-creating curricula that would be meaningful. The article concludes with student exemplars that offer insights into the value of focusing on local artists in order to better meet Art Education curriculum goals in Ontario and, by extension, elsewhere in Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".