FUCK YOUR FEELINGS: THE AFFECTIVE WEAPONISATION OF FACTS ANDREASON
Bibliographic record
Abstract
This paper examines emerging trends in fact signaling: the performative invocation of the idea of Fact and Reason, distinct from the concrete presentation of evidence or reasoning, as a way to cultivate affective solidarity. Emblematic is the conservative influencer Ben Shapiro’s slogan, “facts don’t care about your feelings”: a paean to the mythological figure of emotionlessly objective truth which may then be weaponised against one’s enemies. Scholars are increasingly attentive to the ways in which what was once popularised as a ‘fake news’ epidemic is not simply a virulent strain of bad information in a fundamentally rational online ecosystem, but rather a broader crisis and transformation of what counts as truthful, trustworthy and authentic (e.g. Boler & Davis, 2018; also see Banet-Weiser, 2012). Our contribution emphasises the affective and habitual dimension of this phenomenon. Through a close analysis of Ben Shapiro’s content and personal brand, we show how the generic invocation of Fact and Reason cultivates a sense of affective attachment not defined by ideological consistency or, indeed, the actual practice of research or logical reasoning, but rather a particularly masculinised and adversarial ideal of Truth. The payoff is the reassurance and pleasure of a stable subject position from which one’s political opposition may be Othered with impunity. Facts may not care about your feelings, but insisting upon this fact is all about building a certain structure of feeling.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".