Experiencing environment and place through children's literature
Bibliographic record
Abstract
Recent scholarship on children's literature displays a wide variety of interests in classic and contemporary children's books. While environmental and ecological concerns have led to an interest in 'ecocriticism', as yet there is little on the significance of the ecological imagination and experience to both the authors and readers - young and old - of these texts. This edited collection brings together a set of original international research-based chapters to explore the role of children's literature in learning about environments and places, with a focus on how children's literature may inform and enrich our imagination, experiences and responses to environmental challenges and injustice. Contributions from Australia, Canada, USA and UK explore the diverse ways in which children's literature can provide what are arguably some of the first and possibly most formative engagements that some children might have with 'nature'. Chapters examine classic and new storybooks, mythic tales, and image-based and/or written texts read at home, in school and in the field. Contributors focus on exploring how children's literature mediates and informs our imagination and understandings of diverse environments and places, and how it might open our eyes and lives to other presences, understandings and priorities through stories, their telling and re-telling, and their analysis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".