Open Access Article Processing Charges (OA APC) Longitudinal Study 2015 Preliminary Dataset
Bibliographic record
Abstract
This article documents Open access article processing charges (OA APC) longitudinal study 2015 preliminary dataset available for download from the OA APC dataverse [1]. This dataset was gathered as part of Sustaining the Knowledge Commons (SKC), a research program funded by Canada’s Social Sciences and Humanities Research Council. The overall goal of SKC is to advance our collective knowledge about how to transition scholarly publishing from a system dependent on subscriptions and purchase to one that is fully open access. The OA APC preliminary data 2015 Version 12 dataset was developed as one of the lines of research of SKC, a longitudinal study of the minority (about a third) of the fully open access journals that use this business model. The original idea was to gather data during an annual two-week census period. The volume of data and growth in this area makes this an impractical goal. For this reason, we are posting this preliminary dataset in case it might be helpful to others working in this area. Future data gathering and analyses will be conducted on an ongoing basis. We encourage others to share their data as well. In order to merge datasets, note that the two most critical elements for matching data and merging datasets are the journal title and ISSN.
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.047 |
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".