The meaning-making in kindergarten children’s visual narrative compositions
Bibliographic record
Abstract
During a 10-week classroom-based study in a school in western Canada, 17 Kindergarten children had multiple opportunities to learn about how elements of visual art, design and layout in picturebook artwork are fundamental to meaning-making when transacting with this format of literature. Student application of learning about the concepts under study was explored when the children viewed and discussed wordless or almost wordless picturebooks, and when they created their own artwork or visual compositions. Findings from the content analysis of the Kindergarten children’s visual narrative compositions and individual interviews revealed their understanding of how colour, point of view, framing, line to show action, line to show emotion and implied line can be used purposefully by sign-makers to represent particular meanings. Furthermore, application of Halliday’s metafunctions conceptual framework to analyze three focus students’ visual narrative compositions revealed how their semiotic work concomitantly realized the ideational, interpersonal and textual metafunctions. Consistent with the tenets of social semiotics and sociocultural theory, the descriptions of the instructional procedures and student activities convey how the practices in the classroom shaped the students’ visual narrative compositions. The findings enrich understanding of how young children’s knowledge of various semiotic resources can enhance their understanding and interpretations of the kinds of communicative functions realized or fulfilled by various meaning-making resources, and can inform the design of their visual compositions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".