Bibliographic record
Abstract
Over the past three years, you may have noticed that the Journal Issues have become more ‘voluminous’, both in the British Journal of Social Work and other Journals in our profession. Publishers have encouraged us to clear the backlog of papers, which are published on ‘advance access’ and allocated them to Issues. During this process, the publishing reference changes for each paper. Its year of publication may change, too, depending on the backlog of articles a Journal may have not allocated to each of the Issues they publish. As this backlog is now cleared, our issues will return to a more manageable size of approximately twelve to seventeen papers and five book reviews per each of our eight yearly Issues. Going forward, this will also mean that we are less likely to select more than one paper as the Editor’s Choice for an Issue. During the period where thirty to thirty-five papers were allocated to each of the Journal Issues, we regularly selected two to three papers as the Editor’s Choices for each of them. This reflected the quality and the breadth of work we have a joy and privilege to publish in the Journal. In doing so, we were mindful of our ongoing commitment to promote diverse voices in social work knowledge production, including expertise by experience.
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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.236 | 0.179 |
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".