School Social Work Practice and Research around the Globe
Bibliographic record
Abstract
In this column, we introduce a book resource that examines the roles and practice of school social workers in seven selected countries or regions: the People’s Republic of China, Hong Kong Special Administrative Region, Taiwan, Australia, New Zealand, Canada, and the United States. This edited collection, titled School Social Work: Current Practice and Research (Chui, 2013), also highlights the development of school social work in Asia and the Pacific region. Extensive literature exists about school social work in the North American context, but relatively less research can be found in the region of Greater China and Australasia. In this respect, the current column addresses a literature gap, providing an overview of contemporary school social work services in China and elsewhere; it is to be noted that some of their developments are still in the stages of relative infancy. According to Huxtable and Blyth (2002), they recognize the importance of bolstering understanding of the contemporary international school social work landscape and then learning from one another by engaging in intellectual discussions. The resource introduced here aims to facilitate scholars, practitioners, and students to open further debates on the use of school social work around the world.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".