On the Open Road to Universal Indexing: OpenAlex and Open Journal Systems
Bibliographic record
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 dataset 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 97% 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.
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 | 0.044 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.038 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.022 | 0.043 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".