Navigating the “crossroads”: Critical realism as the middle path in critical social work research
Bibliographic record
Abstract
Summary This article navigates the critical crossroads facing contemporary social work, characterized by the schism between the “Empirical Highway” and “Postmodern/Critical Off-Ramp.” By exploring the evolution of scientific thought and its influence on social work—noting the adage that those who do not learn from history are doomed to repeat it, the article advocates for critical realism. This promising metatheory offers a “middle path,” avoiding the well-trodden routes of empirical rigidity and postmodern relativist turn. Critical realism elevates social work to its highest potential, harmonizing with the field's core values to catalyze profound transformation and fulfill its most ambitious ideals. Findings Critical realism synthesizes various research methods within a unified metatheoretical framework, effectively transcending traditional divides such as individual versus structural changes and quantitative versus qualitative methodologies. Rather than eclectically merging research paradigms, critical realism offers a distinctive perspective that acknowledges the inherent partiality and fallibility of all knowledge. This not only fosters collaboration across diverse research traditions but also significantly reshapes the worldview of social workers. Applications Adopting critical realism steers social work onto a transformative, emancipatory path, providing practitioners with tools to analyze and address both individual and structural realities. This approach champions high-impact research that is methodologically rigorous and aligned with social work's goals, focusing on actionable strategies to significantly improve the lives of equity-deserving groups by tackling the structural and underlying root causes of issues, not just effects. This ensures that social work research and practice drive meaningful and enduring policy and societal change.
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 | 0.086 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.018 | 0.110 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".