Similarity indexes for scientometric research: A comparative analysis
Bibliographic record
Abstract
A significant number of papers in the field of scientometrics addressed the comparisons of various similarity indexes. However, there is still a debate on the appropriateness of an index compared to others, beacause of the assessment differences reported in the literature. The objective of this paper is to make a comparative analysis of the five most used similarity indexes for the three scientometric analysis types: co-word, co-citation and co-authorship. A total of 388 papers addressing similarity indexes in scientometric analysis over three decades were retrieved from the Web of Science and examined; of which 49 were retained as the most relevant according to selective criteria. The approach consisted of building cross matrices for the five indexes (Jaccard, Dice-Sorensson, Salton, Pearson, and Association Strength) for the three types of scientometric analysis. For each of these analyses, a distinction is made between papers according to their theoretical or empirical results. Furthermore, papers are classified according to the mathematical formula of the similarity index being used (vector vs non vector). In the 49 relevant papers being selected, the comparative analysis showed that there is still no consensus on the appropriateness of an index for co-word and co-authorship analyses, while for co-citation, Salton is the widely preferred one. The Association Strength is the less covered and compared to other indexes for the three analysis types. An open source computer program was developed as a tool to facilitate empirical comparative studies of indexes. It allows generating normalized matrix of any chosen index for the two mathematical variants.
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 | 0.075 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.062 | 0.102 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".