A Proposed Method for Residual Citation Allocation Based on Citation Contexts’ Similarity
Bibliographic record
Abstract
Abstract This article proposes an approach for allocating residual citations to scientific publications and demonstrating this proposed approach with a sample of biomedical publications. Residue citations (i.e., citations that are lost due to citation practices termed “Obliteration by Incorporation” and the “Palimpsestic Syndrome”) in consequent citations in the second, third or nth generations are then reconstituted. The proposed approach takes into account citation contexts (i.e., the contribution of a cited publication) for allocating residual citation. The proposed method for allocating residual citation is based on the similarity between the citation contexts of a publication and those of its nth generation citations in their n+1th generation citations. The proposed method was demonstrated using a sample with ten base articles and their five generations of citations, from which 5,272 citation context pairs were obtained. The proposed indirect citation weighting was compared with the existing cascading citation weighting method using one T-test. Statistical tests were also performed to understand the differences in the residual citations from one generation to the other. Like the cascading citation system, residual citations from articles to their generations of citations decreased as the number of generations increased. However, residue citations accrued to publications at all the generations were statistically different between the proposed residual citation and the cascading citation system. This study proposes a method for assessing scientific communication based on the contribution of scientific publications beyond the conventional direct citation.
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How this classification was reachedexpand
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".