Poetry in Digital Times: A Didactic Proposal for the Use of Instapoetry in the EFL Context
Bibliographic record
Abstract
Over the years, the internet has evidenced its proper language and influenced contemporary literature. Motivated by this context, this research focused on what has been called Instapoetry: an emerging digital genre characterized by free minimalist verses (Oliveira & Fazano, 2020) sharable on the social network Instagram. The gist of this study was to promote the literary literacy of eighth-grade students in the English classes in a private school in the city of João Pessoa, state of Paraíba, Brazil. At the same time, learners would be motivated to produce literary texts sensitive to themes that affect their daily social issues. Therefore, interventionist action research was done based on contributions from authors like Stringer (2014), Cosson (2021), Daniels and Steineke (2004), Kato (1990) and Leffa (1996), amongst others. The data obtained allowed a qualitative interpretive analysis. The proposal included the reading of the poem Broken English by Rupi Kaur, considered one of the harbingers of the genre. Before carrying out the study, a questionnaire was applied, to trace the students’ profiles as social network users. The answers revealed that most learners use social networks considerably, especially Instagram. However, without contributing with their own written texts. The didactic sequence with instapoems reading and writing, in its turn, ended up in the genre understanding, discussion on different topics, as well as the production of texts that reflected some themes discussed in Kaur’s poem: the use of the English language to express personal and collective feelings from an emancipatory movement, reflections on the pupils’ origins, prejudices, pain, and hope. Results point to the effective contribution of this didactic proposal to the students’ literary literacy and also to the development of reading and writing processes in the English language.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".