The matter of the stick: Storying/(re)storying children’s literacies in the forest
Bibliographic record
Abstract
Children's intra-actions with the natural world offer an important lens to revisit notions of literacies. They allow for a decentring of humans – here children – as actors. Also, forest schools and nature-based learning programmes are increasingly erupting across North America, although more commonplace in Europe for a longer period. In this presentation of our research, we feature a storying/(re)storying of data from a yearlong research study of children's entanglements with the forest as a more-than-human world. We ask what we might learn if educators, children and researchers think with sticks, not separate from, but in relation to sticks? Eight preschool children, two educators and two researchers ventured into the forest twice a week over the course of a year, documenting their interactions with a mosaic of data generation tools, such as notebooks, iPads, Go-Pro cameras. The forest offered diverse materials that provoked “thing-matter-energy- child-assemblages” that were significant for the children's play and literacy framing. Through post-humanist theorizing, we have paid particular attention to the stick within the children's forest play and illustrate the ways in which the stick was entangled with children’s bodies, relations, identities and discourses. The stick was a catalyst, a friend, a momentary and changing text, an agentic force acting relationally with children's play and stories. The post humanism storying/(re)storying of the children's encounters in the forest with sticks invites infinite possibilities for literacy teaching and learning. How might educators foster such relations, enquiring with and alongside children with an openness toward what the sticks (forests) might teach us?
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".