Charity Open Access Fund (COAF) open access spend and compliance monitoring: 2015-16
Bibliographic record
Abstract
This dataset contains details of the 2015-2016 Charity Open Access Fund (COAF) open access spend. During this period the members of COAF were Wellcome Trust, Arthritis Research UK, Breast Cancer Now, British Heart Foundation, Bloodwise, Cancer Research UK and Parkinson’s UK. In addition to reporting spend, the data has been analysed through our “compliance monitoring” tool (developed for us by Cottage Labs) to help us determine programmatically whether the paper is in the Europe PMC repository and what licence (if any) is attached to the article. The dataset includes information when an article processing charge (APC) was levied to the COAF fund. If an author has self-archived a paper, this information is not included in this dataset. Equally, data are not included in cases where a researcher (based at an institution not in receipt of a COAF block grant) received a supplement to their grant to cover OA publishing costs. We hope that this data will be of use to help better understand the cost of OA publishing. If through use of this data you identify errors or believe the status of an article to be incorrect please notify Hannah Hope (h.hope[at]welcome.ac.uk). We will endeavour to investigate these issues and correct errors where identified. Corrections to the dataset will be published as a new version of the dataset along with note explaining the changes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.029 | 0.008 |
| Open science | 0.024 | 0.021 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.276 | 0.003 |
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; both teacher heads 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".