Inventing a Language Online: The Practice of Edutainment in English Teaching Instagram Posts
Bibliographic record
Abstract
ABSTRACT As the use of English in digital spaces continues to grow, there is an increasing amount of research focused on how it is adapted to local contexts. Proceeding from the argument that we need to go beyond studying English in isolation to investigate how it gets localized in digital settings, the purpose of the study is to investigate Instagram English teaching posts intended for an Iranian audience. To this end, we collect and analyze a dataset of Instagram posts aimed at teaching English. The research questions center on the resources which come together in these posts. Theoretically, the analysis draws on language assemblages. The findings show that English is entangled with a range of other resources and these Instagram posts emerge as engaging in edutainment, a material activity involving three interlined semiotic processes. While these processes do involve drawing from the users' L1s, they are more complex, involving a more varied set of resources than language. The social practice of edutainment comes to the fore as central, and forms are enlisted to serve this practice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".