Walking Softly in the Bush: Apprenticing to the Earth in an Ecological Curriculum
Bibliographic record
Abstract
In this piece, we conceptualize walking in the bush as an act of ecological apprenticing. Even after many years of learning/teaching, we also attune to the limits of our knowledge, seeing ourselves as continually evolving in our practice of nurturing more ethical and responsible apprenticeship relations, both out in the bush and in the classroom. Together, we write about places in the bush that are sacred to us, places around the Nicola Valley and the Nehalliston in the interior of British Columbia. We undertake a holistic and relational dialogue, grounded in life writing and literary métissage (Erika Hasebe-Ludt, Cynthia Chambers & Carl Leggo, 2009). We interpret the work of an ecological curriculum through four interrelated concepts of apprenticing: 1) as a sustained and lifelong, imperfect and unfinished practice; 2) as learning/teaching through sensory heartful attunement; 3) as teaching/learning through wonder; and 4) as a gift which creates relationships and obligations (“bound by legal agreement”). Drawing on Vern’s lifelong pedagogical work of “walking in the bush” and Jodi’s poem entitled “Huckleberry Prayer,” we undertake an intergenerational dialogue around an ecological curriculum as an act of apprenticing to the Earth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".