OpenAlex Author Name Disambiguation V3 Data - Disambiguation Model
Bibliographic record
Abstract
5 Separate files used in the OpenAlex (https://openalex.org) V3 Author Name Disambiguation Model Creation: ORCID_hard_negative_pairs: Pairs of ORCIDs where either the full name, family name, or given name are a match and would therefore be more difficult to disambiguate. Disambiguator_all_possible_training_data: Dataset created which contains all possible features for modeling and all possible samples of data. Eventually, this was split into train/val/test and also processed more to create a better balance of positive to negative samples for our purposes. Disambiguator_final_train_data: Final data which the disambiguator was trained on. Disambiguator_final_val_data: Data which was used to test the model during training to optimize the features/hyperparameters chosen. Disambiguator_final_test_data: Final dataset which gave model performance indication after all hyperparameters were tuned and features were chosen. More details can be found at https://github.com/ourresearch/openalex-name-disambiguation
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.108 |
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".