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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".