Bibliographic record
Abstract
This article features a case study of the written and illustrative text produced by one Grade 7 student, Stefinia, and discusses the metaleptic transgressions evident in the book she created as the culminating activity of a research project. Stefinia was a participant in a classroom-based study that explored how developing students’ knowledge of literary and illustrative elements affects their understanding, interpretation, and analysis of picturebooks and graphic novels, and the subsequent creation of their own print, multimodal texts. As well as being informed by narrative theory and metafiction, the research was framed by an ecological perspective on teaching and learning in classrooms. During a 10-week period, Stefinia participated in interdependent activities that offered her opportunities to learn about metafictive devices, some art elements, and a few compositional principles of graphic novels. Stefinia read and wrote responses to several picturebooks and four graphic novels; engaged in small group, peer-led discussions about the literature; and received explicit instruction about particular literary, illustrative, and compositional devices and techniques. She had the opportunity to apply and represent her learning by creating her own multimodal print text as the culminating activity of the study. The content analysis of Stefinia’s written and illustrative text focuses on her use of various metafictive devices that disrupted narrative structures or ontological boundaries in her multimodal book. The findings reveal how Stefinia’s participation and engagement in a particular classroom community of practice affected her learning of the content and concepts under study.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.580 | 0.362 |
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".