Bibliographic record
Abstract
Welcome to our special COVID-19 issue of the Journal of Community Safety and Well-Being.We recognized early in the pandemic that there would be much for everyone to learn, and we hoped that, among the scholars, policy-makers, and practitioners that comprise our journal community, we might find some willing to contribute to this global learning, even as they were adapting daily to new challenges at home and at work.The response has exceeded our expectations, and this special issue is our largest to date, by far.I would like to extend my appreciation to the many authors who have contributed with thoughtful and urgently relevant content, and to our Section and Contributing Editors and all of our Reviewers who helped us to complete the publication cycle in record time while maintaining high editorial standards.I also want to acknowledge the incredible team at SG Publishing.Not only have they moved double our usual number of papers through to readiness during difficult personal times, they have also planned, designed and executed our transition to a whole new look and functionality for our Journal site.Our OJS 3 upgrade officially launches today in conjunction with this special issue.The COVID crisis is far from over, and I can assure you the Journal will continue to feature relevant pandemic material in subsequent issues, even as other critical, emerging social priorities continue to form before our eyes.In the meantime, I encourage our Readers to dig deep into the 15 articles that comprise this issue.Thanks to all of you for contributing to this vital CSWB dialogue in challenging times.Our open call for papers continues.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.498 | 0.382 |
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".