Conceptualizations of writing in early years curricula and standards documents: international perspectives
Bibliographic record
Abstract
Abstract In this article, researchers in the field of early writing identify underlying beliefs and values about writing and learning to write for the beginning years of formal schooling in four jurisdictions: the American state of Connecticut, New Zealand, the Canadian province of Ontario, and Sweden, as reflected in the respective curricula and standards documents that guide instruction. Using Ivanič’s Discourses of Writing and Learning to Write to guide our text analysis, we found that curriculum developers have primarily been influenced by views of writing as a set of skills, processes, and genres. We found few references to the sociopolitical discourse which indicates a view among curriculum developers that sociopolitical literacy is not suitable for this age group. We argue, with support in previous research, that young children's writing does not have to be politically neutral and that it can be developed under age‐appropriate circumstances. Implications for policy and curriculum development include a need for greater consideration of the complexities of writing shown in research conducted across five decades. We propose a change to the model for early years, recognising that young children's socio‐political understandings lie within their home and school lives, rather than the broader community.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".