Bibliographic record
Abstract
In permanently introducing this new section to our periodical, we wish to call the reader’s attention to a unique approach we are consciously taking. In a desire to identify impending foci in our field, we have invited the youngest of our colleagues – MA and PhD candidates in social work – to act as our reviewers. Furthermore, considering the vast multitude of scholarly articles published annually, we have asked our students to primarily focus on this segment which is more likely to reflect the most recent findings. That said, we have not set a strict date range in the hope that our reviewers will freely discover or recover studies which might have been overlooked heretofore. Chambon A. (2009). What Can Art Do for Social Work? “Canadian Social Work Review”, 26, 2: 217–231. Reviewed by Ana Filipa Rodrigues Banks S., Cai T., de Jonge E., Shears J., Shum M., Sobočan A.M., Strom K., Truell R., Úriz M.J., Weinberg M. (2020). Practising Ethically during COVID-19: Social Work Challenges and Responses. “International Social Work”, 63 (5): 569–583. Reviewed by: Anna Szargiej Van Leeuwen B. (2017). To the Edge of the Urban Landscape: Homelessness and the Politics of Care. “Political Theory”, 46 (4): 586–610. DOI: 10.1177/0090591716682290. Reviewed by: Brygida Piech
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.333 | 0.371 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".