Bibliographic record
Abstract
The focus of this poster is to highlight the importance of sufficient metadata in ORCID records for the purpose of name disambiguation. In 2017 the authors counted ORCID iDs containing minimal information. They invoked RESTful API calls using Postman software and searched ORCID records created between 2012–2017 that did not include affiliation or organization name, Ringgold ID, and any work titles. A year later, they reproduced the same API calls and compared with the results achieved the year before. The results reveal that a high number of records are still minimal or orphan, thus making the name disambiguation process difficult. The authors recognize the benefit of a unique identifier that facilitates name disambiguation and remain confident that with continued work in the areas of system interoperability and technical integration, alongside continued advocacy and outreach, ORCID will grow and develop not only in number of iDs but also in metadata robustness.
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 | 0.026 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".