The simultaneity of experience: cultural identity, magical realism and the artefactual in digital storytelling
Bibliographic record
Abstract
Abstract This paper explores how students, as multimodal storytellers, can weave powerful narratives blending modes, genres, artefacts and literary conventions to represent the real and imagined in their lives. Part of a larger ethnographic case study of student writing in a middle years class for immigrant students learning English as an additional language, the research featured in this paper is framed by a theory of artefactual literacies, narrative theory – particularly the genre of magical realism – and cultural studies, specifically notions of representation and cultural identity. The theoretical emphases on the artefactual, structural and representational aspects of multimodal narratives informs a multilayered, fine‐grained approach to analysing students’ digital narrative poems using the tools of critical discourse analysis, literary analysis and a visual analytic framework developed for analysing student‐produced digital photographs. This process is applied to a selected example, Gabriel's ‘My Name Is’ narrative, a story that plays with elements of magical real‐ism to explore the simultaneity of his experience as an immigrant youth. The illustrative example speaks to the power of the ‘fantastical’ in literacy pedagogies that seek to take seriously students’ cultural identities and their visions for new realities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".