Adding value to repositories through overlay journals
Bibliographic record
Abstract
In June 2018, two math professors, Timothy Gowers (University of Cambridge) and Dan Kral (University of Warwick), in collaboration with Queens' University in Canada, launched a peer-reviewed mathematics overlay journal built entirely on articles contained in the arXiv repository hosted at Cornell University. 'Advances in Combinatorics' is a journal that is free to read and will not charge authors to publish. The relatively low costs of running the journal are being covered by Queen's University Library, which is also providing administrative support. As we continue our progress towards 100% open access, it is critical that we develop sustainable and quality models that have no costs to authors. The presentation will provide an overview of the broader vision for academy-owned open access and next generation repositories, present the model of the overlay journal 'Advances in Combinatorics', and discuss any issues and challenges identified through the work to date.
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 | 0.018 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.028 | 0.053 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".