«INTELLECTUAL DARK WEB» AND PECULIARITIES OF PUBLIC DEBATE IN THE UNITED STATES
Bibliographic record
Abstract
The article focuses on the «Intellectual Dark Web», an informal group of scholars, publicists, and activists who openly opposed the identity politics, political correctness, and the dominance of leftist ideas in American intellectual life. The author examines the reasons for the emergence of this group, names the main representatives and finds that the existence of «dark intellectuals» is the evidence of important problems in US public discourse. The term «Intellectual Dark Web» was coined by businessman Eric Weinstein to describe those who openly opposed restrictions on freedom of speech by the state or certain groups on the grounds of avoiding discrimination and hate speech. Extensive discussion of the phenomenon of «dark intellectuals» began after the publication of Barry Weiss’s article «Meet the renegades from the «Intellectual Dark Web» in The New York Times in 2018. The author writes of «dark intellectuals» as an informal group of «rebellious thinkers, academic apostates, and media personalities» who felt isolated from traditional channels of communication and therefore built their own alternative platforms to discuss awkward topics that were often taboo in the mainstream media. One of the most prominent members of this group, Canadian clinical psychologist Jordan Peterson, publicly opposed the C-16 Act in September 2016, which the Canadian government aimed to implement initiatives that would prevent discrimination against transgender people. Peterson called it a direct interference with the right to freedom of speech and the introduction of state censorship. Other members of the group had a similar experience that their views were not accepted in the scientific or media sphere. The existence of the «Intellectual Dark Web» indicates the problem of political polarization and the reduction of the ability to find a compromise in the American intellectual sphere and in American society as a whole.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".