The body as misinformation – examining the role of bodily information in the formation of false health beliefs
Bibliographic record
Abstract
Introduction. There is scant research into the convergence of embodied information and misinformation. This paper aims to address that gap by examining the intersection of information embodiment and health misinformation. Method. Literature from LIS and adjacent disciplines is synthesised to develop the concept of embodied health misinformation, with particular attention paid to how cognitive biases can influence the formation of false health beliefs based on bodily signals. Analysis. Although bodily information offers valuable insights into health, the body can act as a site of misinformation generation and substantiation due to breakdowns in interoceptive accuracy and cognitive biases in information processing that apply to bodily information. Results. Embodied health misinformation is identified as a significant avenue of future study in LIS because of its potential to illuminate the intractability and deeper epistemic conflicts that underpin health misinformation. Conclusion. A deeper understanding of how misinformation is born and lives within the body can facilitate more sensitive study and evaluation of health misinformation.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".