Stop, collaborate and listen: Reimagining & Rebuilding the Royal Alberta Museum
Bibliographic record
Abstract
The field of museum education is continually examining and reconsidering how best to engage child audiences, offering child-centered experiences to complement knowledge-rich environments. The implementation of Reggio Emilia approach-based programs and activities, which embrace children’s multiple literacies and provide opportunities for free, unstructured play, are best served when complemented by documentation in order to render learning visible to all audiences. It is through documentation that we can actively demonstrate our respect and value for children’s learning and play. Play has to be honoured and celebrated in its own right, and the act of documentation needs to be incorporated into daily operations so it becomes a natural part of the museum experience, and a natural part of evaluation practices. The Royal Alberta Museum has recently undergone a large-scale renewal project; staff sought inspiration from these Reggio Emilia-based philosophies in designing a space that will welcome play and value it as learning, reframing the museum educator’s role as one that documents, collects and curates children’s learning experiences on the gallery floor. In this way, our museum will continue to shape the visitor experience in a ways that place children’s contributions at the forefront – in the way that Elee Kirk imagined.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".