Gaps in Measurement: Highlighting Anti‐Fat Bias as an Underrepresented Construct in the Modified Weight Bias Internalization Scale
Bibliographic record
Abstract
ABSTRACT Increasing cross‐sectional literature highlights a strong overlap of internalised weight bias (i.e., weight‐based self‐devaluation) with constructs like body dissatisfaction. The highest overlap is in studies that use the Modified Weight Bias Internalization Scale (WBIS‐M). We argue that anti‐fat bias (e.g., negative judgements of fat people) is a core feature of internalised weight bias definitions not represented in the WBIS‐M, making its items less distinct from body dissatisfaction. To investigate, we examined the longitudinal relationships of anti‐fat bias with internalised weight bias among 3025 sexual minority men using random intercept cross‐lagged panel models. We contend that, if the WBIS‐M adequately captures anti‐fat bias, these constructs should be strongly associated across time. To the contrary, we found medium cross‐lagged (longitudinal) relationships of these constructs over time ( β s 0.07–0.08), and a small between‐person association of these constructs ( β = 0.10). The limited strength of these effects suggests that the WBIS‐M does not adequately capture anti‐fat bias to the extent that existing definitions suggest it should. Researchers must be cognisant of what measures capture, and consider what scales most appropriately capture the components of weight stigma they want to assess.
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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 | 0.057 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".