Bibliographic record
Abstract
This paper explores tensions between scientific understandings of the internet phenomenon known as Autonomous Sensory Meridian Response (ASMR) and accounts of the experience put forth by people who experience ASMR (also known as ASMRers). While current scientific research into the therapeutic affordances and the physiological, neurological, and psychological determinants of ASMR have failed to produce satisfactory accounts of the experience, ASMRers label and describe the phenomenon in scientific terms to give the experience scientific validity. So far, this strategy has worked, infusing a series of scientific inquiries into the strange uniqueness of the experience. That said, efforts to understand the ASMR experience through modern scientific, technological, and conceptual strategies is not only limiting, but futile. ASMR is incomprehensible from the standpoint of modern scientific discourse because of the unique, posthuman constitution of the ASMR body. This has led to what I call the ASMR Paradox: growing efforts to describe ASMR, a scientifically inaccessible experience, in purely scientific terms. In consideration of this paradox, the following reflection piece explores the tension between scientific discourses regarding ASMR and the seemingly diametrical experiences of ASMRers. I conclude that, while the former is indebted to western humanist thinking, the latter expresses a posthumanist configuration that is incompatible with the scientific rhetoric currently being used to describe it.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".