Bibliographic record
Abstract
The aim of this study was to review existing knowledge on policy practice (PP) in social work education that seeks to train undergraduate and graduate social work students to influence social policy. The review, based on different search strategies, identified 113 publications written by scholars from Australia, Canada, Israel, South Africa, UK and the USA. The review revealed marked growth in interest in PP education between 1970 and 2014. This was reflected in articles that have reported on six areas: research on the place of PP in social work curricula; research on students’ preferences with regard to PP; recommendations on what to include in PP education; descriptions of actual pedagogical methods and courses; evaluations of actual pedagogical methods; and tools for assessing PP competencies. Most of this discourse was devoted to descriptions of PP courses and teaching methods and much less to systematic evaluation of PP teaching. Key Practitioner Message: • Policy practice education is playing a growing role in social work training at both the Bachelor of Social Work (BSW) and Master of Social Work (MSW) levels; • The literature suggests a wide range of innovative experiential approaches in class and in actual policy arenas; • To further strengthen the field, systematic evaluations of policy practice teaching innovations are required.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".