Fact versus feeling: What post-truth scholarship can learn from the feminist phenomenology of affect
Bibliographic record
Abstract
Although it is a relatively new phenomenon, the most popular descriptions of post-truth operate within the boundaries of the classical dichotomy between emotion and reason that dates back to Plato’s Phaedrus: both, to some extent, view emotions as impediments to knowledge and our ability to live morally upstanding lives (248a-b). Post-truth, which is seen as a threat to reason, social cohesion, and fact-based knowledge claims, is either viewed as the outcome of the failure of our cognitive apparatus, or a consequence of our unchecked thirst for stories that provoke dramatic feelings. From a feminist point of view, this should give us pause, since the arguments used to dismiss post-truth resemble those that dismissed women’s experiences and emotions as idiosyncratic or irrational. Have post-truth scholars been too hasty in their judgment of emotion-based knowledge claims? In this essay, I explore the transcendental role of emotion in its relationship to epistemic knowledge claims and argue that emotion should be given a more primordial status in the analysis of post-factuality. I do this by exploring the psychoanalytic and phenomenological analysis of affect, especially Sara Ahmed’s feminist phenomenology of embodiment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".