Platformization, social media, and investing in unequal digital literacy practices
Bibliographic record
Abstract
Abstract This paper examines how the platformization of social media and the material conditions in which these technologies are used shape how transnational learners invest in unequally valued digital literacy practices. As platforms reconfigure cultural practices and imaginations, L2 learners negotiate online norms and conventions to participate agentively in these spaces, positioning themselves while also positioning others. Based on data from a case study involving 16 immigrant Filipino secondary school students in Canada, this paper highlights how various inequalities of access, participation, and representation circumscribe social media practices. Findings show how the material designs of Facebook, Instagram, and Snapchat encourage patterns of interaction that become the basis of legitimate participation in these platforms, privileging certain aesthetic norms and class-inflected tastes. Learners are socialized into these conventions in ways that can exclude peers who do not have access to particular resources or whose semiotic productions are not deemed valuable. This asymmetric distribution of resources shapes practices that provide contrasting opportunities for L2 use and expansion of social networks. Tracing these inequalities to platformization, this paper proposes digital repertoires and digital socialization as constructs to draw attention to how learners develop and enact digital literacies that are valued unequally and that shape different opportunities for learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".