Bibliographic record
Abstract
Raising one's jammed fist is not just a common pictorial representation of struggle against the establishment but turns out to reflect a deeper connection between sensorimotor states and beliefs. The present research investigated how physical firmness, manipulated through muscle tightening, might shape beliefs. The central hypothesis, structural alignment, was tested against two other competing predictions: content extremity and content matching. Three studies provided evidence supporting structural alignment but not content matching or extremity. Specifically, the correlation between intelligence and personality lay beliefs (Studies 1-2), and the correlation between individualizing and binding moral foundations (Study 3) increased when participants jammed their fist (Study 1) or clenched their jaw (Studies 2-3). These effects emerged in the absence of mean-level differences (which would have reflected content matching or extremity). Moreover, they did not seem attributable to response bias or tiredness. An additional study suggested decent rates of compliance with experimental instructions that were comparable between conditions. Overall, sensorimotor experiences such as physical firmness can align higher-order cognitions such as beliefs in ways that are distinct from prior demonstrations of embodied cognition effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".