Bibliographic record
Abstract
This paper describes a two-part study. The first part of the study documents the experiences of nine school-based artists who took part in a six-day professional development workshop on ecology and the arts at an off-grid wilderness facility. The course was designed to increase artist-educators’ awareness of issues surrounding energy use and consumption as well as to provide them with direction for approaching these topics through arts-based learning in schools. One of the sessions introduced an electronic portfolio as a tool for supporting arts-based learning on topics dealing with ecology and the environment. Data were collected from the artist-educators through observations, field notes, and semi-standardized interviews. Participants also completed an on-line survey regarding various energy conservation and consumption issues. The data for the first part of the study revealed how the artist-educators anticipated interacting with teachers and students upon returning to their local schools, both in terms of content related to energy conservation and in the ways that they would approach this topic through their respective art forms. In the second part of the study, the electronic portfolio was used to promote self-regulated learning with Grade 5 students in a public elementary school in Toronto, Ontario while they studied energy and ecology through an arts-based approach to these topics. Students worked with one of the participants who took part in the six-day professional development workshop. Together with their teacher, they explored solar power and wind turbine energy and presented their learning through dance. As for the first part of the study, classroom-based data were comprised of observations, field notes, and interviews. In addition, a pre- and post-questionnaire was used to ascertain the degree of self-regulatory practices used by the students. Students expressed considerable enthusiasm for the tool and demonstrated significant growth in understanding how to set goals and critique the work of their peers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".