Understanding research for social policy and social work : themes, methods and approaches
Bibliographic record
Abstract
Introduction Research for social policy and social work Ethical conduct and research practice Formulating research ideas and questions Methodological issues and approaches Quantitative research Qualitative research Dissemination, knowledge transfer and making an impact Glossary List of contributors: Pete Alcock - University of Birmingham, Charles Antaki - Loughborough University, Karl Ashworth - Office for National Statistics, Mary Baginsky - Children's Workforce Development Council, Matt Barnard - Head of Evaluation NSPCC, Sarah Banks - Durham University, Fiona Becker - NSPCC, Saul Becker - University of Nottingham, Peter Beresford - Brunel University, Nigel Bilsbrough - Loughborough University, Annette Boaz - Kings College London, Joanna Bornat - The Open University, Jonathan Bradshaw - University of York, John D. Brewer - University of Aberdeen, Alan Bryman - Leicester University, Emma Carmel - University of Bath, Patrick Carmichael - Liverpool John Moores University, Anne Corden - University of York, Louise Corti - University of Essex, Gary Craig - University of Durham, Duncan Cramer - Loughborough University, Christopher Day - University of Nottingham, David Deacon - Loughborough University, David de Vaus - University of Queensland Australia, Mary Dixon-Woods - University of Leicester, Harry Ferguson - University of Nottingham, Ben Fincham - University of Sussex, Jerry Floersch - Rutgers University School of Social Work New Jersey USA, Lynn Froggett - University of Central Lancashire, Rachel Fyson - University of Nottingham, Graham R. Gibbs - University of Huddersfield, David Gordon - University of Bristol, Hilary Graham - University of York, Martyn Hammersley - The Open University, Mark Hardy - University of York, Alexa Hepburn - Loughborough University, Michael Hirst - University of York, Lesley Hoggart - University of Greenwich, Lisa Holmes - Loughborough University, Sally Holland - Cardiff University, Annie Irvine - University of York, Stephen Joseph - University of Nottingham, Savita Katbamna - University of Leicester, Ravi KS Kohli - University of Bedfordshire, Jane Lewis - London School of Economics & Political Science, Pranee Liamputtong - La Trobe University Australia, Jeffrey Longhofer - Rutgers University of School of Social Work New Jersey USA, Clare Madge - University of Leicester, Reima Ana Maglajlic - Swansea University, Nicholas Mays - London School of Hygiene and Tropical Medicine, Stephen McKay - University of Birmingham, Bren Neale - University of Leeds, Carolyn Noble - Victoria University Melbourne Australia, Henrietta O'Connor - University of Leicester, Stewart Page - University of Windsor Canada, Jan Pahl - University of Kent, Alison Park - National Centre for Social Research, Elizabeth Peel - Aston University, Cassandra Phoenix - University of Exeter, Robert Pinker - London School of Economics and Political Science, Jennie Popay - Lancaster University, Catherine Pope - University of Southampton, Jonathan Potter - Loughborough University, Stephen Potter - The Open University, Colin Robson - University of Huddersfield, Karen Rowlingson - University of Birmingham, Roy Sainsbury - University of York, Jonathan Scourfield - Cardiff University, Clive Seale - Queen Mary University of London, Joe Sempik - University of Nottingham, Elaine Sharland - University of Sussex, Ian F Shaw - University of York, Janet Smithson - University of Exeter, William Solesbury - ESRC UK Centre for Evidence Based Policy, Bruce Stafford - University of Nottingham, Patricia Thomson - University of Nottingham, Peter Townsend - formerly University of Bristol, Harriet Ward - Loughborough University, Samantha Warren - University of Essex, David Westlake - Loughborough University, Sue White - University of Birmingham, Sharon Witherspoon - The Nuffield Foundation, Ruth Wodak - Lancaster University
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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 | 0.061 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".