Author name disambiguation for collaboration network analysis and visualization
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 In this paper we outline a heuristic algorithm for disambiguating author names of publications via deterministic clustering based on well‐defined similarity measures between publications in which their names appear as authors. The algorithm is designed to be used in the construction of a collaboration network, i.e., a graph of author nodes and co‐author links. In this context, the goal is to produce a co‐authorship graph with network characteristics that are close to those of the “true” collaboration network, so that meaningful network metrics can be determined. The algorithm we present here is fairly easily comprehended as it does not depend on any sophisticated AI techniques. This is important in the context of policy studies, in which we successfully applied it, as it enables policy makers to judge the soundness of the methodology with considerable confidence. It is also quite fast, making it possible to run large‐scale analyses (here, in the order of a hundred thousand publications and in the order of a million names to be disambiguated) on a moderately sized desktop computer within a few days. The algorithm is, finally, open to improvement via extensions that take into account additional kinds of fields in bibliographic records of publications to provide evidence that two occurrences of similar names belong to the same individual.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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