Human Growth and Development An Introduction for Social Workers
Bibliographic record
Abstract
'This is a much welcome book providing an engaging, informative and accessible introduction to human growth and development for social workers...(It) will undoubtedly be invaluable reading for social work students, a learning companion during social work training and an interesting and enjoyable read for those already in practice.' - Gabriela Misca, Keele University, UK \n \n'The book skilfully instructs and interacts with the reader. It presents information in a way that will appeal to learners at different stages offering both introductory and in-depth levels of knowledge. These unique features make this book an indispensable text for student social workers in learning and practice environments.' - Nora Duckett, London Metropolitan University,UK \n \n'John Sudbury has provided a human growth and development textbook full of interest. Written specifically for social work, readers will be able to engage with it at various levels - from the personal impact of narratives, case studies and reflective questions, to the more in depth academic content in outlines of key theoretical issues.' - Ruben Martin, University of Kent, UK \n \n'The author’s interest in psychosocial perspectives and his rich experience as practitioner, manager and academic social worker/researcher ensure the quality and rigor of this text.' - Clare Parkinson, Senior Lecturer, University of East London, UK
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.016 |
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".