Using Teacher Dialogue to Bring Nature Based Learning to Kindergarten: The Hippo Inquiry
Bibliographic record
Abstract
During a three month period, partner and student teachers were involved in a hippo inquiry project with two classes of kindergarten children. Their collaborative investigation centered upon pursuing this essential question: How are people living respectfully with hippos? They did so by learning about the Calgary Zoo’s support of the Wechiau Community Hippo Sanctuary (WCHS), a unique commu-nity-based project, protecting and pre-serving the wildlife and environment of a 40km stretch of the Black Volta River in Ghana’s upper west area. Nurturing a classroom climate that invites young children to explore gen-erous possibilities and make discover-ies inherent in an emergent curricu-lum was integral to this work. Together the teachers explored differ-entiated teaching strategies which helped them work effectively with children of diverse backgrounds, interests and skills. Valuing connec-tions with the world outside the class-room motivated this teacher team to invite a zoology professor, a zoo edu-cation director, and a naturalist to deepen this nature based inquiry.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".