Visual Subject Analysis for Dublin Core Research / L'analyse visuelle de sujet aux fins de la recherche Dublin Core
Bibliographic record
Abstract
The primary purpose of this article is to conduct a subject analysis on Dublin Core research, investigate subject topics related to Dublin Core research, and reveal their dynamics over time. Documents related to Dublin Core research were identified in authoritative and comprehensive databases of Web of Science, and subject terms were extracted from the relevant documents. These raw terms were regularized, and the multidimensional scaling (MDS) visualization analysis method was applied to reveal semantic relationships among subject terms. The temporal analysis on the related subject terms added a unique dimension to the study. Three periods (from 1997 to 2001; from 2002 to 2006; and from 2007 to 2011), in addition to the entire period (from 1997 to 2011), were analysed and compared in the visual contexts. Obsolete topics, newly emerging topics, and basic topics on Dublin Core research were identified and analysed in temporal subject analysis. Topic changes in the three periods are shown. The findings of this study reveal the hidden patterns of subject associations, illustrate themes of Dublin Core research and their dynamics over time, and shed light on the understanding of Dublin Core research.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".