Rankings, Ruling and Reproducing Inequities: Critiquing the Knowledge Production of Social Work’s “Top 100 Scholars”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reflecting on an article authored by Hodge and Turner (2023) that ranks the “top” 100 social work scholars, this article presents a multi-layered critique of the tradition of using bibliometrics to generate “knowledge” and competitive global rankings of individual social work faculty members, departments and universities. We raise concerns regarding the transformation of neoliberal metrics into social work research questions and projects, and then solidified into competitive, martketised knowledge about social work and its scholars. We argue that through this process, inequity and neoliberalism are normalized and legitimized, and we are further distanced from social justice, decolonization, and equity. The article provides alternative assessments grounded in community participation and social justice and aimed at expanding equity and social justice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it