A Study on Paper and Author Ranking
Bibliographic record
Abstract
As the number of journal issues, conferences and the overall scientific literature have been increasing at an exponential rate, it has become challenging for researchers to find appropriate and useful papers from the vast literature available to them. To solve this issue citation count, h-index, i10-index are used to rank authors. In 1998, Brin and Page introduced the algorithm PageRank which is also used in the scientific community for ranking research papers and authors. However, each of these metrics has its own drawbacks. We hypothesize that papers unveiling deeper truth are often not as well cited as those that are more challenging for a wider number of authors to assimilate and appreciate their works. So a simple count of the number of citations may fail to capture the essence of the quality of a paper. With a view to addressing this issue, we have introduced a new algorithm that also takes into account the quality of the researcher citing an article, and considers it in ranking. We have carried out experiments. While the experiments are not as comprehensive, results have been incorporated. They look promising in ranking authors and papers that are not cited too often due to difficulty in understanding them.
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 | 0.016 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.039 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".