Development and Validation of a Measure of Attitudes toward Fluffy Women
Bibliographic record
Abstract
BACKGROUND: There is an absence of research on the newly evolved term "fluffy" which describes body image and personality features among women. Research on "fluffiness" among Caribbean peoples has been limited by the lack of valid and reliable measures of the concept. OBJECTIVE: This project addresses this problem by exploring the internal consistency reliability and the concurrent and discriminant validity of the Attitudes toward Fluffy Women Scale (ATFW) using a mixture of past and present students from The University of the West Indies (UWI), Mona, and the University of Technology (UTech), Kingston. METHOD: Past or present students from The UWI, Mona, and UTech, Kingston, were recruited for the study through the use of convenience sampling. A total of 80 students (38 males, 47.5%; 42 females, 52.5%) participated in the study. RESULTS: Overall, the ATFW was found to have an acceptable degree of internal consistency reliability (α = 0.90). The scale also had reasonably good concurrent validity as evidenced by moderate correlations with scores on the Attitudes Toward Obese Persons Scale (r = -0.42) and acceptable discriminant validity as demonstrated through low correlations with a Bogardus Social Distance Scale designed to assess prejudice toward people living with the human immunodeficiency virus [HIV] (r = 0.29). This pattern of scores suggests that the majority of the stable variance underlying the ATFW assesses the "fluffy" concept (17.6%) while a smaller degree of the variability (8%) measures a conceptually similar but distinct concept. CONCLUSION: The Attitudes toward Fluffy Women scale was found to be a reliable and valid scale for assessing the attitudes of young adults toward fluffy women.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".