From Academia to Algorithms: Digital Cultural Capital of Public Intellectuals in the Age of Platformization
Bibliographic record
Abstract
Scholars traditionally hold influential positions due to their cultural capital, derived from academic degrees, scholarly publications, and professional credentials. However, the rise of digital platforms has disrupted this hierarchy, placing scholars into new roles as online public intellectuals who engage in political advocacy and mobilize knowledge through public discourse. This transformation calls attention to how public intellectuals’ visibility and influence have become entangled with platform logics, leading to a reconsideration of “digital cultural capital”. Drawing theoretical insights from critical platform studies, celebrity studies, and marketing research, this article conceptually addresses three questions: (1) how traditional cultural capital transforms digitally; (2) how public intellectuals accumulate digital cultural capital; and (3) what are the risks of knowledge mobilization on platforms? This article proposes that traditional academic credentials are no longer sufficient to maintain public intellectuals’ influence, whereas visibility metrics—such as views, likes, shares, and follower counts—emerge as a digital form of “cultural capital from below”. Public intellectuals, thus, must engage in “code-switching” to navigate platform-mediated knowledge mobilization. Nevertheless, the populist tendencies embedded in cultural capital from below and the platform algorithms that enable it risk marginalizing less visible knowledge forms. Eventually, this article calls for future empirical research on how digital cultural capital and code-switching operate across geopolitical contexts, particularly within marginalized communities shaped by distinct platform logics and populist dynamics.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".