A Land Exploration-Based Approach
Bibliographic record
Abstract
In a Community School located in a First Nation in Northern Ontario, grade 3 students draw on their connection with the land and their own background knowledge through a Land-Exploration-Based Approach to learn about structures and their functions. This method allowed students to become primary investigators in their own learning. In preparation for a summative engineering task, students first engaged in various activities to promote both cultural and scientific understanding. An outdoor Snowshoe Discovery Walk provided the opportunity for students to independently identify various structures found in nature along with the co-creation of working classroom definitions. A comparison of natural and human-built structures found in their surroundings was implemented to further develop foundational learning. In the classroom, students engaged in learning about and constructing beaver dams to cultivate essential engineering and design skills. The culminating activity was introduced through the integration of Indigenous stories to foster cultural relevance in students as they partook in designing and testing an animal shelter of their choosing. This holistic approach to teaching effectively engaged students, promoted curiosity, and built on their knowledge of structures and functions all while developing collaboration and problem-solving skills. Next steps are directed at solution improvement in the design process.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".