Re-citation Analysis: A Promising Method for Improving Citation Analysis for Research Evaluation, Knowledge Network Analysis, Knowledge Representation and Information Retrieval.
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
Citation analysis is used in research evaluation exercises around the globe, directly affecting the lives of millions of researchers and the expenditure of billions of dollars. It is therefore crucial to seriously address the problems and limitations that plague it. Central amongst critiques of the common practice of citation analysis has long been that it treats all citations equally, be they crucial to the citing paper or perfunctory. Weighting citations by their value to the citing paper has long been proposed as a theoretically promising solution to this problem. Recitation analysis proposes to tune out the large percentage of perfunctory citations in a paper and tune in on crucial ones when performing citation analysis, by ignoring uni-citations (mentioned just once in a paper) and counting and analyzing only re-citations (used again and again in a citing paper). By focusing on core connections in knowledge networks, re-citation analysis can help research evaluation become more sensitive to the distinction between essential and perfunctory impact of research. It may benefit citation-link based knowledge representation and retrieval systems with improved precision by better capturing “aboutness” of articles, the essence of subject indexing in knowledge representation and retrieval, rather than merely providing “relatedness” information. Conference Topic Theory; Methods and techniques
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 | 0.192 | 0.176 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.132 | 0.500 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 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