Sociotechnical structures, materialist semiotics, and online language learning
Bibliographic record
Abstract
Based on a study of the digital literacy practices of immigrant Filipino students in Vancouver, this paper focuses on how learners with unequal access to resources engage with different tools to locate information and find opportunities for language learning online. Data was collected through interviews and observations of participants as they used YouTube, Google Search, and Google Translate to decode unfamiliar words and find resources for learning. Framed through a materialist semiotic lens, this study examined how the students negotiated their resources on these platforms to achieve different intentions. Findings show that the way learners navigate these spaces can vary based on the devices they use (laptop vs. mobile phone), the user interface (browser vs. app), and the orientation they choose (landscape vs. portrait). The material dimensions of the screen determine the arrangement of semiotic forms, and varying configurations of devices, interfaces, and orientations shape the information made available to the learner and the digital literacy practices of scrolling, clicking, and shifting tabs. Recognizing how the online environment of a platform can shift across these layers of mediation, this paper conceptualizes the linguistic and semiotic forms that constitute design as sociotechnical structures which provide various learning affordances and constraints.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".