Bibliographic record
Abstract
Handbook Preface. Karen M. Sowers. Catherine N. Dulmus. Volume Prolusion. Barbara W. White. Contributors. 1. The History of Social Work and Social Welfare. Leslie Leighninger. 2. Educating for Social Work. Julia M. Watkins. Jessica Holmes. 3. The Scope of Social Work Practice. June G. Hopps. Tony B. Lowe. 4. Professional Credentials and Professional Regulations: Social Work Professional Development. Donna DeAngelis. Mary Jo Monahan. 5. Social Work Organizations. Gary Lowe. Terry Singer. 6. Values and Ethics for Professional Social Work Practice. Kimberly Strom-Gottfried. 7. The Strengths Perspective: Putting Possibility and Hope to Work in Our Practice Dennis Saleebey. 8. Child Welfare: Historical Trends, Professionalization and Workforce Issues. Robin E. Perry. Alberta J. Ellett. 9. Family-Based Practice. Barbara Thomlison. 10. School Social Work. Cynthia Franklin. Beth Gerlach. Amy Chanmugam. 11. Substance Abuse. Lori K. Holleran Steiker. Samuel A. MacMaster. 12. The Mental Health Field of Practice. King Davis. 13. Healing the Disjuncture: Social Work Disability Practice. Elizabeth DePoy. Stephen Gilson. 14. Gerontology: A Field of Practice. Roberta R. Greene. Namkee Choi. 15. Forensic Social Work: Current and Emerging Developments. Katherine van Wormer. Albert Roberts. David W. Springer. Patricia Brownell. 16. International Social Work. Doreen Elliott. Uma A. Segal. 17. Immigrant and Indigenous Populations: Special Populations in Social Work. Jon Matsuoka. Hamilton I. McCubbin. 18. Diversity. Iris Carlton-LaNey. 19. Social and Economic Justice. Tricia B. Bent-Goodley. 20. Putting Evidence-Based Practice into Practice. James G. Barber. 21. Making Social Work Practice More Scientific. Allen Rubin. 22. Issues in Social Work. Stanley I. Witkin. Roberta Rehner Iversen.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.208 | 0.124 |
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".