Reconceptualizing semiotic resources in the eco-social system of an online language tutoring course
Bibliographic record
Abstract
Translanguaging and trans-semiotizing research has problematized the static view of language and argued that meaning making is a dynamic, material, social, and historical process across multiple timescales in complex eco-social systems. The second author proposed the concept of trans-semiotizing as an alternative lens to study language teaching and learning. In this autoethnographic study, the dynamic processes of online language learning and teaching are examined by analysing the semiotic resources, trans-semiotic practices, and the coordination of different semiotic resources. To capture such dynamic processes and the semiotic resources involved, the first author setup multiple cameras and used screen recording to document my teaching. Data include recordings of my computer screens, video recordings of my physical environment, facial expressions, body movements, screen shots of my social media posts, and my teaching notes. We draw on Lemke’s dynamic eco-social system concept to discuss how semiotic resources are used in online language teaching and learning across different timescales.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".