Methods and Strategies to include nature in science: Engaging with nature in the lab, and outdoors
Bibliographic record
Abstract
N ature in school is most frequently encountered in science classrooms and labs. This science education activity, in the Advanced Professional Term (APT) for secondary science (EDSE 455) at the University of Alberta, compares how students engage with nature in the science lab, and outdoors. In this activity, students interact with living organisms in the science lab. Students then go outdoors to interact with similar organisms they find. When students reflect on their interaction and engagement with nature in the two settings, they typically report touching, poking, prodding and ‘playing with’ the nature in the classroom/science lab. For example, students frequently breaking off the aloe leaves to use the moisturizing sap in the plant. Students rationalized this behaviour by stating that “these are specimens”, and practices, such as control or manipulation, is part of “doing science”. Students typically report only observing the plant or insect they selected outdoors, stating this behaviour came from beliefs that this was “nature” and they didn’t want to harm or disrupt it. If school science aims to produce environmentally sustainable dispositions in students, student teachers need to be engaged in activities that identify, criticize, and challenge school science practices that are detrimental to nature.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".