Core Concepts from Multiliteracies for Language Teachers in Contemporary Times
Bibliographic record
Abstract
First paragraph: Three nine-year-old boys are sitting on a porch in urban Canada. They are engaged in a multiplayer session of Terraria, a video game that purports to combine the creativity and freedom of a sandbox environment with the strategic requirements of an action game. Each child is holding his own device—an iPod Touch, an iPad, an android tablet. Their eyes are fixed on their own screens, sometimes scanning over to the others’, fingers busily pushing and swiping as they build biomes. During the game, one of the boys opens an Internet browser, types in a term from the game, and the children collectively research how to find an element they want. Through the search results they read blog posts from other players and add their own information to the mix. All the while they are playing, the boys are talking away to each other. If you were to listen in and focus on the discourse, you’d hear all seven of Michael Halliday’s functions of language: instrumental (“I want to build…”), regulatory (“Do this here and…), interactional (“Let’s…”), personal (“Watch me when…”), informative (“When you go here…”), but especially heuristic (“What happens when you…”) and imaginative (“In this world…”). Given such events, literacy research has been grappling with questions like, what is literacy in this new communicational landscape (e.g., is video gaming a literacy practice?) and what are the implications of the response to this question for education?
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".