Teaching poetry using Instagram: An international, interdisciplinary study with adolescents mobilizing literacy and the arts
Bibliographic record
Abstract
Young people engage daily with various social media platforms to communicate with each other across the globe. Adolescents not only share text, but also use images and sound to express themselves on platforms such as Instagram and TikTok to provide access to user-created content. The recent emergence of InstaPoetry—poetry with images on Instagram—has been part of such communication and provides a good entry point into adolescents’ engagement with literature and the arts. Limited research exists, however, on how this literate practice, paired with virtual and in-person museum visits, influences young people’s self-expression. In this article, we offer ways of integrating and involving these dynamic dimensions into research projects based on four sites of inquiry located in Canada and Australia. Funded by the Social Sciences and Humanities Research Council, this research project provides concrete avenues to investigate teachers’ methods to foster adolescents’ engagement with literature and the arts (i.e., contextual design, procedures, environment) in (post) COVID-19 times.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".