Bibliographic record
Abstract
Educators today are being asked to design curricula whereby learners’ abilities to analyze, question, problem-solve, evaluate and reflect are being provoked. The quest lies in uncovering suitable teaching approaches that will allow critical thinking skills to emerge organically and meaningfully. I argue that an integration of loose parts can offer a methodology and a provocation that makes way for open-ended, divergent and creative thinking skills to be activated. “Loose parts” can be open-ended materials that are manipulated, designed, dismantled and reconstructed in multiple ways. I also see “loose parts” as a mindset, a process-oriented approach whereby meaningful conversations emerge unexpectedly and add significantly to learning. This article presents two stories to show how arts-based approaches and mindfulness to loose parts can unearth thought-filled and caring conversations. The discussion is inspired and written via a reflective lens of personal encounters, first, in a longitudinal research project with young children in an Indigenous First Nations Community, and, second, with preservice teachers in a university class. It is within these periods that students, teachers and families were impacted by loose parts whereby materials and conversations made way for new perspectives in understanding the world.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.106 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".