OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts
Bibliographic record
Abstract
In May 2021, Microsoft announced that it was discontinuing support for Microsoft Academic Graph, a free and widely-used Scientific Knowledge Graph (SKG). This was met with considerable concern, as MAG was viewed as difficult to replace with existing systems. The OpenAlex project was created to address this concern. It launched as a drop-in replacement for MAG contemporaneously with MAG’s retirement on January 1st 2022. Although still in its nascency, as a fully-open (100% open data, open API, open-source code) source of scholarly metadata, OpenAlex has potential to improve the transparency of research evaluation, navigation, representation, and discovery, adding to the growing list of other open and partly-open SKGs such as OpenCitations, AMiner, PID Graph, Open Research Knowledge Graph, Semantic Scholar, and the OpenAIRE research graph.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.065 |
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".