Filter bubbles and guru effects: Jordan B. Peterson as a public intellectual in the attention economy
Bibliographic record
Abstract
In this article, we critically reflect on the role of Canadian psychologist Jordan Peterson as a public intellectual in an increasingly hybrid, interconnected, and plural public sphere. In today’s attention economy, and in the face of a general climate of scepticism and crisis of expertise, we are faced with the limits of the liberal ideal of the public intellectual who filters information for the public. In fact, we argue, the public intellectual can in some cases come to function as a creator of filter bubbles instead of furthering democracy. We analyse Peterson’s writings and public performances in order to illustrate this, focusing on the particularities of his rhetoric and dramaturgical strategies. First, we discuss his misreadings and misrepresentation of ‘postmodernist’ thought. Then, we examine the non-verbal aspects of his performance, in order to unpack his appeal by examining his affective strategies. Last, we apply theory from the cognitive sciences, most notably relevance theory and the guru effect, to examine Peterson’s rhetoric and the strategies he uses to inspire trust in his audience.
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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 | 0.003 |
| 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 | 0.000 |
| 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; 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".