Bibliographic record
Abstract
Everyday racism embedded in all facets of society, coupled with ongoing injustices against racialized people globally, have reignited an urgent action to turn the gaze within social work education. There is a need to challenge and resist white supremacy that continues to institutionalize systemic racism and justify state control of social and political processes. These current realities are in direct contradiction to the neoliberal push for state withdrawal from social programming and essential services. Yet the interconnectedness between neoliberalism, white supremacy and fascist ideologies has gone undetected in social work circles resulting in a political and ideological vacuum in the profession. Within the social work curricula, there is a lack of attention and involvement to effectively dismantle white supremacy and racism that are perpetuated within and through the profession. The social work classroom has been a natural place to incubate a new wave of resistance that has the potential of changing the face of the profession. Considering the deleterious effects white supremacy has for racialized bodies within academic spaces, we assert the embodiments of resistance with a call to action for social work scholars, students, administrators and practitioners. These key actors must reject the legacy of white supremacy in our profession that acts as social control agents serving the state's interests and perpetuating its hegemony. We explore some of the ways in which we confront and disrupt white supremacy, which includes interrogating and dismantling dominant discourses, systemic and institutional academic racism (teaching, research and service), social work curriculum and priorities, and racist classroom dynamics that have been shaped by whiteness that continues to impact the interactions between racialized and white students and professors. We conclude with a call to infuse social work with practices and approaches that equip students with knowledge and tangible tools to enact change beyond academic spaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.067 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".