Exact versus estimated pruning of subject hierarchies
Bibliographic record
Abstract
Abstract Many large digital collections are organized by subject; these useful information organization structures are large and complex, thus difficult to browse. Current online tools and visualization prototypes show small localized subsets and do not provide the ability to explore the predominant patterns of the overall subject structure. This study builds on existing work concerning automatic subject hierarchy modification techniques that aim to facilitate browsing for documents by capitalizing on the highly uneven distribution of real‐world collections. Specifically, previous work used an estimation of the number of accessible documents offered by each subject term, while the current study uses the exact number of accessible documents. The impact is demonstrated on a large collection organized using Medical Subject Headings (MeSH). Results show that, although computationally more demanding, pruning the MeSH hierarchy based on the exact access produces a different subject hierarchy under some conditions. The visual impact is demonstrated using traditional outline views. This study has implications for the development of information organization theory and human‐information interaction techniques for subject hierarchies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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, 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".