Mapping Languaging in Digital Spaces: Literacy Practices at Borderlands
Bibliographic record
Abstract
The study presented in this article explores the ways in which discursive-technologies shape interaction in digitally-mediated educational settings in terms of affordances and constraints for the participants.Our multi-scale sociocultural-dialogical analysis of the interactional order in the online sessions of an Italian for Beginners language course provided by a university in Sweden is illustrated in terms of an Introduction phase, a Language and Grammar phase, a Discussion phase, and a Concluding phase.Dimensions of TimeSpace shape the organization of the lessons where a range of literacy practices can be identified.A second step in the analysis zooms into the Discussion phase.Taking the concepts of epistemic engine and epistemic domains as points of departure, we explain how the written word shapes the interactional order in online settings.This study highlights how different interactional orders allow for the opening up of new socialization spaces, in which students are more likely to be prevented from getting trapped in their own script of task-oriented activities.Here, participants' cultural processes are complexly layered in digitally-mediated encounters, where their focused orientation towards a variety of offline and online oral and written resources is partly curtailed by the digital environment itself.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".