Bibliometric factor maps for knowledge discovery in digital libraries
Bibliographic record
Abstract
In this paper we describe the architecture of a visual bibliometric browsing plug-in for the growing number of digital libraries that provide cited references in their document meta-data, using a simple but effective visualization method for citation network analyses we recently introduced. Citation-based network analysis methods such as co-citation analysis have long been recognized as effective tools for gaining insight into the intellectual structure of a field through its literature. Visualizations of these networks can help the user get an intuitive aggregated overview of the field and the interrelationships between documents or authors, which in turn can aid query expansion, search refinement, and exploratory browsing. Our design calls for a visualization of the results of a multivariate factor analysis of a bibliometric similarity matrix calculated from a user's search results and/or from documents that are closely related to them. This provides the user a digital library with an interactive map of the literature that the user is interested in, where each visual element aggregates different aspects of the search result (authors and/or subfields). By helping the user see the forest for the trees (i.e., a structured visual landscape of the intellectual domain covered by the user's search and its bibliometric vicinity rather than a long list of search results), these maps and the relevant links they contain promise to provide a valuable aggregated browsing tool for digital libraries.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".