Role of Social Media in Addressing Educational Inequality: A Critical Examination of Marginalized Teens’ Social Media Usage
Bibliographic record
Abstract
Although digital technology is valorized for its promises in empowering individual learners and democratize educational opportunities, such hopeful imaginaries need to be critically revisited. What is the role of social media in addressing educational inequality? This study aims to unravel the question by examining the role of social media in the college choice system of potential first-generation college students. The study adopts a multiple case study approach, engaging with eleven high school seniors, whose parents did not complete a four-year college/university. Two interviews and a week of social media diary data were collected. The themes revealed the teens’ contradictory views toward the role of social media in their college choice system: (a) abundant but insufficient information, (b) helpful but not impactful for college choice, and (c) inspiring but distressing experiences. The contradictions uncover the important role of in-person support system and resources embedded in marginalized teens’ college choice system, underscoring the pre-existing inequalities in their social contexts. Based on the results, I critically discuss the role of social media in addressing educational inequality, particularly the optimism around digital informal learning, and provide suggestions for formal schooling to enhance marginalized teens’ college access.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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 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".