Tree Stories: Assessment Making as Relationally Respecting, Dynamically Listening with Care, and Inviting Celebratory Journeying
Bibliographic record
Abstract
Through my recent Master of Education journey, I am becoming increasingly awake to the ways in which I value “place” when thinking of my autobiographical beginnings, teaching, learning, and assessment making alongside children. As a forest and nature kindergarten teacher in a K–6 elementary school, who is facing increasing class sizes, classroom complexity, parental anxieties, and children’s wide-awakeness to all these influences, I worry about early childhood development and the knowing that each of us, from an early age, holds embodied experiences which guide us and either nurture or hinder our development. I see the importance of co-creating environments to invite children, parents, and educators to think collaboratively, and emergently to come to learning and knowing and assessment making. Basso (1996) writes, “relationships to places are lived most often in the company of other people, and it is on these communal occasions – when places are sensed together – that native views of the physical world become accessible to strangers” (p. 109). We teach and learn in social contexts across time and in the world of formal education on landscapes called schools. However, my hope is for schools to be thought of in a broader context, which bends and sways as we ground ourselves in experiential pedagogies (Dewey, 1938). Pedagogies which are always in the midst and in the making. Narrative beginnings matter. With this thinking, we become awake to knowing that our collective approaches to the education of young children will significantly affect future leaders and the environment which sustains us. My current research puzzling for the purpose of this special issue is as follows: in what ways can land-based, place-based, emergent, and playful experiential learning approaches in school outdoor settings increase child, family, and teacher collaboration? What role might assessment making have in this collaboration and the child’s learning? Do the stories that children come to live and tell through this collaboration and their dynamic interaction with one another, their families and teachers, and more-than-human beings influence the lives they are making in the present in their early years of schooling i.e., K–3, and into their futures?
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".