Hey Siri: Should #language, 😕, and follow me be taught?: A historical review of evolving communication conventions across digital media environments and uncomfortable questions for language teachers
Bibliographic record
Abstract
This article presents a study on novel language forms and uses across evolving digital environments, and questions whether emerging digital communication conventions should have a place in language education. The study was motivated by the deepening gap between the content of and approaches to language instruction evident in popular mobile-(assisted) language learning (MALL) apps and the sophisticated evolutions in digital communication over the past 30 years. A team of researchers conducted an environmental scan to locate academic journals publishing on digitally-mediated language and language teaching/learning applications, and to determine topical themes and discussions. This scan was followed by a collaborative in-depth focused literature review to document technological advances and evolutionary changes in social communication across the lifespan of the WWW. The authors posit that language teaching theory and practice must attend to digital convergence and posthumanism, and pose uncomfortable questions for the language teaching profession, such as: What is the place of conversational digital agents in language teaching? Should new media grammar forms be specifically taught? Who is the arbiter of appropriate language use in digital communication?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".