Bibliographic record
Abstract
This paper presents findings from an eleven-year ethnographic study which describes how three children used different sign systems to become literate, to define who they are and to construct their literate identity. They each engaged with literacies in powerful and life transforming ways. Each child used multiple literacies to learn, understand and create meaning more fully; using their motivated interest in a preferred literacy to scaffold their learning of another literacy.In analysing this rich literacies use I have come to understand that literacies are complex in their conception and use and that all sign systems (e.g. art, dance, reading, writing, videogaming, etc.) operate using common semiotic principles. Sign systems as literacies are multimodal, meaning-focused and motivated; they involve specific social and cultural practices which differ depending on site and community. During every literate act the children in this study made extensive use of the semantic, sensory, syntactic and pragmatic cuing systems to make meaning, regardless of the literacies used.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| 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".