Bibliographic record
Abstract
To support our authors, reviewers and editors during the COVID-19 pandemic, the Spectrum Editorial Board has relaxed its timelines for the publication of this most recent issue (Issue 5). We are working with our authors on their Issue 5 submissions, and will publish continuously into Issue 5 over the remainder of Summer 2020. Please check back often for new articles, which will be added to Issue 5 as they are finalized. At this time, Issue 6 (Fall 2020) submissions are in the review process, and we anticipate publication in late fall, as we transition to a new editorial team. If you are an undergraduate or graduate student interested in joining the Spectrum editorial team for 2020-2021, we encourage you to submit your application here, by July 31, 2020. Peer Reviewer applications are accepted year-round - see the “Become a Reviewer” page for more information. We thank our authors, reviewers, and readers for their patience and continued support, and we hope you enjoy the latest issue! The Spectrum Editorial Board
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 | 0.011 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.152 | 0.125 |
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".