Bibliographic record
Abstract
This issue contains nine fascinating papers which, on the surface, have little in common with each other. Yet, to someone like me, coming from mental health social work and an increasingly multidisciplinary, yet user-focused, perspective, which also looks to international perspectives for inspiration, a number of commonalities became apparent. Carolyn Taylor’s paper on narration and reflection which opens the issue sets the scene as to the value of taking an in-depth reflective stance in analysing what clients and workers have to tell each other. Often dismissed as a luxury by practitioners, their managers and those focused on large populations research, this type of analysis reminds us that monologues and potential dialogues lie at the heart of social work. In the second article, the search for the best available knowledge occupies Andrew Long, Lesley Grayson and Annette Boaz in their attempts to identify criteria with which to judge the quality of social care knowledge. Coming from social policy rather than from social work backgrounds, and overlooking the multi-stakeholder nature of social care itself, the authors embark on the delineation of seven generic criteria for evaluating knowledge. Assessing the value of knowledge is important when different knowledges and evidence need to be considered, and the usefulness or otherwise of the scheme they propose deserves to be widely discussed.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.338 | 0.207 |
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".