“eLoriCorps Immersive Body Rating Scale”: Exploring the Assessment of Body Image Disturbances from Allocentric and Egocentric Perspectives
Bibliographic record
Abstract
The first objective of this study was to test the convergent and discriminant validity between the “eLoriCorps Immersive Body Rating Scale” and the traditional paper-based figure rating scale (FRS). The second objective was to explore the contribution of the egocentric virtual reality (VR) perspective of eLoriCorps to understanding body image disturbances (BIDs). The sample consisted of 53 female and 13 male adults. Body size dissatisfaction, body size distortion, perceived body size, and ideal body size were assessed. Overall, outcomes showed good agreement between allocentric perspectives as measured via VR and the FRS. The egocentric VR perspective produced different results compared to both the allocentric VR perspective and the FRS. This difference revealed discriminant validity and suggested that eLoricorps’ egocentric VR perspective might assess something different from the traditional conception of body dissatisfaction, which an allocentric VR perspective generally assesses. Finally, the egocentric VR perspective in assessing BIDs deserves to be studied more extensively to explore the possibility of finding two types of body image distortion: (a) an egocentric perceptual body distortion, referring to internal body sensation affected by intra-individual changes, and (b) an allocentric perceptual body distortion, referring to external body benchmarks constructed by inter-individual comparison occurring in a given cultural context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".