Bibliographic record
Abstract
Social work is a profession based on social justice and anti-oppressive action. Social workers in direct practice in most states must possess a master’s level graduate degree and have completed internship/practicum placements hours. This higher education and professional development prerequisites within graduate curriculums must begin to include increased acceptance and understanding of diversity and social justice through expanded lenses and move beyond current frameworks of diversity. For example, one such area of diversity that has become an increasing proportion of higher education students are students who identify as obese or “fat,” and are experiencing oppression and stigma in their everyday life. The authors used a grounded approach to analyze 100 accredited graduate social work programs’ curriculum throughout the United States, with the goal of understanding how the topic of obesity and weight-based oppression were integrated into learning curriculums of diversity, social justice, and cultural humility. The authors will discuss “fat culture” and stigma associated with an obese identity, as well as systems that are inherently oppressive to people who are of larger size. The findings revealed evidence of professional disregard for this population in practice, as well as overarching disregard for body size as a dimension of diversity and inclusion on an institutional level. The authors will reflect on these findings and discuss implications for practice, knowledge, and professional and educational pedagogy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".