Platformization, social media, and investing in unequal digital literacy practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it