Uncovering Literacy Narratives through Children's Drawings
Bibliographic record
Abstract
Children’s drawings about reading and writing have unrealized potential for helping uncover the literacy narratives students bring to school and use to make sense of reading and writing. In this article, we highlight how one boy’s drawing about literacy revealed his interpretation of his school’s policy on violence as a topic of writing, which tended to constrain his interest in writing. His drawing reinforced the importance of adopting multiple perspectives to interpret the various texts that students produce. Keywords: multiliteracies, children’s drawings, multimodal representations Les dessins d’enfants traitant de la lecture et de l’ecriture offrent un potentiel inexploite pour la decouverte des recits au sujet de la litteratie que les eleves apportent a l’ecole et dont ils se servent dans leur eveil a la lecture et a l’ecriture. Dans cet article, nous mettons en relief comment le dessin d’un garcon au sujet de la litteratie revelait son interpretation de la politique de son ecole sur la violence comme sujet de redaction, laquelle avait tendance a restreindre son interet pour la production ecrite. Son dessin renforce l’importance d’adopter des perspectives diversifiees lors de l’interpretation des divers textes que les eleves produisent. Mots cles : multilitteracies, dessins d’enfants, representations multimodales
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".