Rankings, Ruling and Reproducing Inequities: Critiquing the Knowledge Production of Social Work’s “Top 100 Scholars”
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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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 | 0.155 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.045 | 0.027 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.033 | 0.024 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| 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".