Preventing Trivialization of Global Social Justice Discourse: A Framework of Critical Social Media Literacy
Bibliographic record
Abstract
Growing scholarship emphasizes social media as potential platforms to facilitate social justice discourse, while others document simultaneous polarization and trivialization. With the objective of supporting educators, students, and community members, this article conceptualizes a framework of critical social media literacy (CSML) based upon Freirean pedagogy. While social media platforms exist in diverse formats, social media numbers in the form of likes, shares, and views emerge as common elements to influence social justice ideologies. Building upon this preliminary conceptualization, the framework proposes that students may benefit from understanding how social media numbers can: grant automatic, unfounded legitimacy; be mobilized toward not social justice but the pursuit of profit; be a prerequisite for visibility of content; replace critical thinking as ubiquitous metrics; lead to false sense of disorienting dilemmas; facilitate antagonization of nuanced perspectives; encourage conformity; and be directly purchased. Implications on conformity—and expanded thinking for students—conclude the study.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".