Bibliographic record
Abstract
MAKIN AND JONES DIAZ (2002) suggest that young children's early literacy is most strongly supported when early childhood educators, children's families and the wider community develop shared understandings about literacy —what counts, what is valued and validated, and whose voices are heard and whose voices are silent. Researchers such as Haas Dyson (1993), Kress (1997), and Stein (2003) argue that young children's literacy experiences comprise much more than developing the conventional processes of written language, but also include their use of a range of multiple semiotic symbol systems. This article will explore young children's constructions of literacy, and reveal the important role family members (e.g. parents, aunts, uncles, grandparents) and school staff (e.g. teachers, Elders, speech and language pathologists) play in mediating young children's literacy learning and development.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".