Red Pills, Blue Books: Youth Political Consciousness and the Epistemic Struggle Between YouTube and the University
Bibliographic record
Abstract
What happens when YouTube and the university offer competing visions of political reality? Drawing on 25 interviews, this article explores how young people navigate political meaning in the age of digital capitalism. While conservative influencers on YouTube offer emotionally charged, algorithmically amplified narratives of ‘common sense’, social science classrooms provide tools for critique and structural analysis. But access to the latter is increasingly constrained by tuition, austerity, and ideological attacks. Using a Gramscian lens, I argue that political consciousness is shaped through an epistemic struggle between two asymmetrical knowledge systems: the expansive, frictionless world of YouTube, and the embattled, slow-moving institution of the university. In tracing how political meaning is shaped across these spaces, the article shows how the social sciences can still cultivate ‘good sense’, but only when students are able to reach them.
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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 | 0.002 | 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 | 0.002 | 0.044 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".