Pointing to Shaun Tan's <i>The Arrival</i> and Re-imagining Visual Poetics in Research
Bibliographic record
Abstract
In this article, the authors discuss how Shaun Tan's graphic novel The Arrival (2006) opened a polyphonic dialogue with culturally diverse early childhood educators. Using visual, graphic and symbolic languages provided alternative ways for the research participants to express their experiences and understandings of being recent immigrants. Analyzing and interpreting the stories that the participants narrated, the authors noted their linguistic, aesthetic and embodied responses to Tan's visual poetry – specifically the physical act of pointing to the images. The research raises multiple questions for consideration: How might The Arrival (and other graphic narratives) be used as an elucidative prompt for understanding caregiver/teacher practice in early childhood education and in educational research more broadly? How can visual research methodologies enhance the complex interrelations among curriculum, diversity and visuality in early childhood education? And, lastly, how can such imaginal and playful approaches point to deeper considerations around intercultural dialogue, social relations, pedagogy and the curriculum in early childhood education settings? Graphic novels provoke intercultural dialogue in research interviews and point to new research methodologies. They enhance cultural understandings among students, scholars and educators at post-secondary and pre-K-12 levels, providing insights into how culturally diverse educators and students can live and learn together in an always complex world.
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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 | 0.006 | 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 | 0.008 | 0.034 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".