Bibliographic record
Abstract
Abstract A paucity of research has been conducted with learners in elementary classrooms on both the use of and the student creation of science comics. During the classroom-based research featured in this article, Grade 4 students designed ocean threat comics for the culminating activity of an interdisciplinary Ocean Literacy unit, one component of a larger study. Throughout the research, the students were afforded with opportunities to develop their visual meaning-making skills and competences, as well as their aesthetic understanding of and critical thinking about multimodal ensembles through participation in activities that focused on various elements of visual art and design, and conventions of the medium of comics. The visual and descriptive analysis of one student’s ocean threat comics, which includes excerpts from the interview about her work, reveals her motivations for selecting and orchestrating specific semiotic resources to represent and express particular meanings that realized her objectives as a sign-maker. Overall, the descriptions of the pedagogy featured during the research and the student’s ocean threat comics demonstrate how the development of student knowledge about elements of visual art and design, and conventions of the medium of comics can inform and deepen students’ semiotic work of comprehending, interpreting and designing science comics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".