On the open road to universal indexing: OpenAlex and Open Journal Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study examines OpenAlex’s indexing of Journals Using Open Journal Systems (JUOJS), reflecting two open-source software initiatives supporting inclusive scholarly participation. By analyzing a data set of 47,625 active JUOJS, we reveal that 71% of these journals have at least one article indexed in OpenAlex. Our findings underscore the central role of Crossref DOIs in achieving indexing, with 96% of the journals using Crossref DOIs included in OpenAlex. However, this technical dependency reflects broader structural inequities, as resource-limited journals, particularly those from low-income countries (47% of JUOJS) and non-English language journals (55–64% of JUOJS), remain underrepresented. Our work highlights the theoretical implications of scholarly infrastructure dependencies and their role in perpetuating systemic disparities in global knowledge visibility. We argue that even inclusive bibliographic databases like OpenAlex must actively address financial, infrastructural, and linguistic barriers to foster equitable indexing on a global scale. By conceptualizing the relationship between indexing mechanisms, persistent identifiers, and structural inequities, this study provides a critical lens for rethinking the dynamics of universal indexing and its realization in a global, multilingual scholarly ecosystem.
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.
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 | 0.069 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.023 | 0.153 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.023 | 0.002 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it